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An integrative debate on learning styles and the learning process
Lucimar Almeida Dantas
a
,
*
, Ana Cunha
b
a
Integrated Researcher at Interdisciplinary Research Centre for Education and Development –CeiED. ULHT, Campo Grande, 376. 1749 -:024, Lisboa, Portugal
b
Professor at Lus
ofona University, Lisboa, Portugal
ARTICLE INFO
Keywords:
Learning styles
Learning process
Interconnection
Cerebral cortex
ABSTRACT
This paper aims to present a contribution to the debate on learning styles and the learning process discussing some
classic learning styles theories: Kolb’s experiential learning theory and learning style model, Honey and Mun-
ford’s Learning Style Model, Felder and Silverman’s learning styles and the VARK model. We propose to link them
with the learning process by exploring knowledge derived from other areas, such as Biology and the Neurosci-
ences, to broaden the horizons of understanding on the subject. This reflection was developed as part of the
ERASMUS þresearch project IC-ENGLISH –Innovative Platform for Adult Language Education. After exploring
theories, we present a brief view of the interconnection in the cerebral cortex to support our conclusions, sug-
gesting an integration of learning styles approaches for a more successful learning process.
1. Introduction
Learning, in the strict sense of the term, is an essential process for
human beings, for cultures and for the success of educational systems.
Formal education integrates the subjects to their environment, enables
the development of cognitive and social skills, gives access to the cultural
heritage accumulated by the history of humanity, and enables the
advancement of this heritage, through the creation of new knowledge.
With the recent advances and the debate over lifelong learning,
formal adult education has gained space, as well as interest, in the
mechanisms underlying the learning process of this audience. Nowadays,
it is widely accepted that people use different avenues to learn; have
preferences for different stimuli and that they facilitate the learning
process. Thus, while some are comfortable with written texts, readings,
debates, and written output, others prefer images, videos, drawings,
schemes, or practical, reality-centered tasks with a concrete purpose.
According to Cassidy (2004), in the last four decades, many studies
have been conducted on learning styles. Coffield, Moseley, Hall, and
Ecclestone (2004) identified more than 70 learning styles theories
developed in the three decades preceding the study. These theories, in
most cases, correspond to questionnaires, applied on a large scale by the
industry, to identify students’learning styles, the relationship between
students’and teachers’learning styles (Awla, 2014;Massa &Mayer,
2006;Naimie, Siraj, Piaw, Shagholi, &Abuzaid, 2010;Tuan, 2011)
whether by physical or virtual means. Among the best known are Dunn
(1990) Learning Styles Model, Kolb’s (1984, 1985) Learning Styles
Inventory, and Honey and Mumford’s (1992) Learning Styles
Questionnaire.
With the wide dissemination of questionnaires, the expression
“learning styles”has received different concepts and approaches, ac-
cording to the focus chosen by its students (Kazu, 2009), as well as strong
criticism of the scientific evidence of the correlation between learning
styles, methodological choice and improvement of learning (Pashler,
McDaniel, Rohrer, &Bjork, 2008;Scott, 2010). There are also expres-
sions used as synonyms, but which designate different processes. In this
sense, when reviewing the literature in the area, it is common to find
terms like learning style and cognitive style used as synonyms. However,
they have different meanings and relate to different levels in the learning
process. According to James and Gardner (1995, p. 20), learning styles is
“the complex manner in which, learners most effectively perceive, pro-
cess, store, and recall what they are attempting to learn”. Conversely,
cognitive style refers to “an individuals’natural, habitual, and preferred
way (s) of absorbing, processing and retaining new information and
skills”(Reid, 1995: viii).
Learning styles corresponds to “generalized differences in learning
orientation based on the degree to which people emphasize the four
modes of learning process”(Kolb, 1984, p. 76). Among the various
concepts available, we will use Kolb’s (1984) here for the theoretical
support that precedes it and that we present below.
* Corresponding authors.
E-mail addresses: lucimar.dantas@ulusofona.pt (L.A. Dantas), ana.cunha@ulusofona.pt (A. Cunha).
Contents lists available at ScienceDirect
Social Sciences &Humanities Open
journal homepage: www.elsevier.com/locate/ssaho
https://doi.org/10.1016/j.ssaho.2020.100017
Received 18 December 2019; Received in revised form 18 February 2020; Accepted 20 February 2020
Available online xxxx
2590-2911/©2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Social Sciences &Humanities Open 2 (2020) 100017
2. Kolb’s experiential learning theory and learning styles model
Kolb’s learning styles model is supported by Kolb’s Experiential
Learning Theory (ELT), a comprehensive theory of learning and adult
development. Kolb (1984),Kolb and Kolb (2013) explain that ELT is built
on propositions of some prominent scholars, namely John Dewey, Kurt
Lewin, Jean Piaget, Lev Vygotsky, William James, Carl Jung, Paulo
Freire, Carl Rogers and Mary Parker Follet.
For Kolb (1984) and Kolb and Kolb (2013) learning should be
considered a process and not only for the results obtained. It is facilitated
when students have the opportunity to test and retest their beliefs,
knowledge and ideas on a given topic, and add new and refined ideas.
Learning is a holistic process of adaptation to the world that requires the
ability to resolve dialectically conflicts between modes of adaptation to
the world - reflection/action and feeling/thinking. Learning is therefore
the process of knowledge creation which requires the synergy between
social knowledge and personal knowledge.
In the historical-cultural postulate of learning in which the theory is
inserted, Kolb (1984) uses the Vygotskian concept of Proximal Devel-
opment Zone to ground a new concept, that of ‘experiential learning’,
directed towards adult learning. This concept is based on the assumption
that learning is built from the experience lived in context, in interaction
with the knowledge that each individual has already accumulated at a
given moment. Human beings live integrated in natural, cultural and
historical environments that give them the necessary elements to enable
them to construct their knowledge. In this framework, experiences can be
transformed into learning and this, in turn, expands the knowledge that
each one already has. However, not every experience results in learning.
Learning is a mental process oriented toward a purpose, which requires
conscious reflection. Learning is “the process whereby knowledge is
created through the transformation of experience. Knowledge results
from the combination of grasping and transforming experience."(Kolb,
1984, p. 41).
From these assumptions, Kolb (1984) then presents an explanatory
structural model of learning and an instrument of identification of
learning styles, directed to the formation of adult professionals, known as
the Kolb cycle, as can be seen in Fig. 1.
As seen in the diagram, Kolb’s experiential learning cycle represents
an ideal dynamic view of learning, oriented toward dialectically
resolving the two opposite forms of experiencing (reflection/action) and
transforming experiences into knowledge (feeling/thinking).
1. Concrete experience - (feeling) refers to the contact with concrete
problems to be solved, referenced in the accumulated world knowl-
edge and already developed mental structures;
2. Reflective observation - consists of the internal action of identifying
characteristics, grouping and organizing information, establishing
connections, searching for similar concepts, analyzing information
that contributes to the solution of the problem.
3. Abstract conceptualization - (thinking) here is where concepts are
formed and generalized, from the comparison with similar realities,
which results in a synthesized set of knowledge about the problem.
4. Active experimentation - is the external application, in unpublished
practical actions, of the experiences felt, reflected and conceptualized
in the previous phases of the cycle.
The combination of cycle modes gives rise to Kolb’s learning styles
and Kolb Learning Styles Inventory - LSI - (Kolb, 1971,1976) in its
various versions (Kolb, 1985,1999;Kolb &Kolb, 2013).
A learning style is therefore the combination of preferences among
the four modes of learning; the point at which the learner enters the
learning cycle, that is, the individual way in which each individual
combines the two modes of experience/reflectionaction and turns it into
knowledge (feeling/thinking). Since learning is here considered a pro-
cess, in construction and continuous reconstruction, which occurs
through the interaction of the individual’s knowledge with the
knowledge of the environment, the learning style is not a fixed psycho-
logical or cognitive trait, but rather the result of the interaction between
the person and the environment (Kolb &Kolb, 2013). Kolb detects the
following styles:
Diverging - the learning style derived from the CE/RO combination.
Individuals with this style have a preference for visual stimuli, con-
crete situations, combined with diverse information. They feel
comfortable with group work, discussion and constant feedback.
Assimilating - characterised by the preference for visual and mental
(RO/AC) stimuli. Learners with this style deal more easily with
analysis, explanations, theories, texts and all kinds of material that
allow analysis and reflection.
Converging - is the learning style of people who identify with prac-
tical tasks and deductive reasoning to solve a given problem (AC/AE).
These learners have a preference for direct and practical guidance and
learning tasks.
Accommodating - is the learning style identified by the preference
for making plans, projecting the future, creating prospects for situa-
tions, from stimuli involving thinking and doing (AE/CE). Individuals
with this style handle challenging activities easily, take risks, and
solve problems intuitively (Kara, 2009).
3. Honey and Munford’s learning styles model
Kolb’s (1984) experiential learning theory and his model of learning
styles are the foundations for the learning styles model developed by
Honey and Mumford (1992). The Honey and Munford Model - Learning
Styles Questionnaire - LSQ - establishes learning styles from the strategies
used by learners to capture and transform information. They are: Activist,
Reflector, Theorist and Pragmatic, corresponding to the AE, RO, AC and
CE strategies of the Kolb cycle, respectively.
Activist learners learn best in situations of concrete action, where
experimentation, learning by making mistakes and being correct is
favored. Group discussion activities, problem-solving, puzzles and
brainstorming are stimuli that favour the learning of activist individuals.
Reflectors share a style of learning that prefers a combination of
observation and thinking to learn. They consider many possibilities and
implications in an act before taking a decision. Activities that give them
time to investigate and think, go back and observe, review what
happened, without deadlines, are preferred by reflectors.
Theorist learners are more comfortable with learning from explan-
atory models, theories, statistical data, analysis and synthesis. These
learners need to understand the logic behind the actions. Activities of
discussion, reading, case studies, with stimuli that allow them time to
think, seek theoretical explanations, formulate models and base problem
solving are the most suitable for these learners.
Pragmatist learners apply to practice analytical knowledge to create
new things and solve problems. Activities with a clear link between topic
Fig. 1. Kolb’s experiential learning cycle.
Source: adapted from Kolb (1984),Kolb and Kolb (2013).
L.A. Dantas, A. Cunha Social Sciences &Humanities Open 2 (2020) 100017
2
and real need, techniques applied to current problems and clear guide-
lines offer the stimulus preferred by pragmatists.
3.1. Felder and Silverman’s learning styles
Felder and Silverman (1988) developed an instrument to identify
learning styles, i.e. the Index of Learning Styles (ILS). These authors
consider learning styles to be the preferences and qualities of individuals
as they receive and process information. Thus, they established a model
that measures the approximation of preferences between the categories
Active/Reflective, Sensitive/Intuitive, Visual/Verbal, Sequential/Verbal
using a pre-established scale. The ILS underwent several reformulations,
which resulted in the design of a questionnaire (Felder &Soloman, 1991)
to identify students’learning styles in the four categories previously
identified by Felder and Silverman (1988). In 1997, the instrument was
made available on the Internet for free use (Schmitt &Domingues, 2016).
The pairs of learning styles established by ILS are:
Active –these learners prefer to work in a group, strive for learning
from actions; or Passive - prefer to work alone or in small groups;
learn better by thinking about the problems/issues before them.
Sensitive –these learners prefer the concrete, the sensitive, the real
facts; or Intuitive - these are more conceptual, like theories, expla-
nations and syntheses.
Visual –these learners prefer activities involving images, visual
representations; or Verbal - prefer written information, reading and
commentary.
Sequential –these learners prefer processes segmented into well-
defined parts that follow linear thinking; or Global - need a holistic
perspective to process the information.
4. The VARK model
Another instrument used to identify students’learning styles and to
enhance their learning was developed by Fleming (2001), based on
mapping learning styles. According to this author, the VARK technique -
Visual, Aural, Read, Kinesthetic - corresponds to the four channels used
by individuals to receive and process information:
Visual –individuals who favour the visual aspect learn best from
pictorial information and descriptions such as drawings, graphics,
and images. They organize the reasoning better with the use of lists
and diagrams. For these learners, the most indicated activities are
lectures, slide presentations, diagrams, graphics, videos and images,
resolution of exercises, surveys or any other materials that contain
visual information.
Aural - these individuals use the auditory pathway to learn better.
They prefer information with sounds and audio guidance, such as
spoken instructions, discussions, oral presentations, conversations,
music, audio and video information, music and role plays.
Read/writing - learners using this style prefer written and reading
information as a means of learning. They usually resort to notes, di-
agrams and all sorts of writing to learn better. Activities involving
texts, reading, abstraction production, essays, articles, comments or
any other type of written stimuli are preferred by these individuals.
Kinesthetic - people with this learning style need movement, sensory
touch and interaction with the environment to acquire information
and create knowledge. Activities such as hands-on classes, problem
solving, case studies, demonstrations or physical activities are best
suited for learners using this pathway.
5. Integrating approaches
The discussion about learning styles and the effectiveness of their
identification to improve learning may have in Biology and Neuroscience
points of common interest for a broader dialogue. Although they are
different areas from those that focus on the topic of learning styles, the
knowledge produced there can contribute to the enrichment of what we
know about the complex process that is human learning.
Much is known today about brain structure and its functioning,
although scholars in the field recognize that they are still at the beginning
of this discovery process, and that there is much more to know.
In terms of structure, Biology has divided the human brain into
overlapping layers with distinct functions. The most superficial layer is
the cerebral cortex, which is divided into large areas, responsible for
processing the external stimuli that are captured by the sense organs, as
shown in Fig. 2.
Each part of our cerebral cortex specializes in receiving and pro-
cessing stimuli coming from different external points, and then produc-
ing an output. As seen in Fig. 2 above, at the back of the brain is the visual
cortex, responsible for image recognition and formation; next to it is
Wernicke’s area, responsible for understanding spoken or written words.
This, in turn, is connected to the auditory cortex, which receives and
processes sound information in general. Further ahead is Broca’s area,
the part responsible for speech articulation; it is here where the muscles
are activated for the production of speech. The higher mental functions
(concentration, planning, judgment, expression of emotions, creativity)
are processed in the prefrontal cortex, while the motor cortex processes
and activates motor actions and the sensory cortex takes care of sensitive
information.
Note that specific cortexes (visual, auditory, sensory, motor) integrate
broader regions, areas of visual, auditory, motor and sensory association,
respectively; that is, there are specific centers for the different informa-
tion, but these work within a broader specific area. It is this integration
that enables, for example, recognition of forms or texture, understanding
language signs, planning motor actions and modulating sensory impulses
(Orhan &Arslan, 2016). Despite the specialty of each of the areas, it is
known that they work intensely interconnected to transform the infor-
mation captured by the sense organs into knowledge.
In a simplified example of the path between an auditory input and the
verbal output equivalent to the question “What day is it?”and an
appropriate answer, we would have the following process: 1. The sound
is picked up by the auditory area and sent to Wernicke’s area, where the
comprehension of the spoken language is processed, namely, the mean-
ing of the words, their sequence in the sentence. In order to do so, 2.
memory is used, that is, knowledge already accumulated about the lan-
guage and the world. Next, 3. The motor area and Broca’s area come into
play, which voluntarily trigger a set of muscles in the throat and face to
vocalize the appropriate response.
The interconnection between the areas of the brain is confirmed by
studies in Neuroscience. Research carried out by Dehaene, Spelke, Pinel,
Stanescu, and Tsivkin (1999) on exact additions (e.g. 3 þ4¼7) point to
the activation of the left-lateralised area in the inferior frontal lobe, an
area of the brain commonly associated with language. Conversely, when
the additionwas approximate (e.g. 3 þ4¼8), this area did not show any
activity. According to the researchers, this is because exact additions
involve the retrival of knowledge on the number intensively acquired
previously. This information is usually stored in the areas of the brain
responsible for language.
Another study conducted by Shaywitz and Shaywitz (2005), on young
adults followed longitudinally, found different types of neural paths to
support reading. The authors used brain images to analyse the neural
connections of three groups: 1. Persistently poor readers (PPR) thus
named for their low reading skills at the beginning of the study, in the
2nd and 4th grades, and at the end, in 9th and 10th grade. 2.
Accuracy-improved poor readers (AIR), thus characterised for meeting
the criteria for poor reading at the beginning of the study, but not at the
end; and 3. Nonimpaired readers (NIR), those who showed no evidence
of poor reading performance at any stage of the study. Analysing the
brain connections developed by the three groups, the researchers found
different neural paths: NRI readers demonstrated connectivity between
the left hemisphere posterior and anterior reading systems. In contrast,
L.A. Dantas, A. Cunha Social Sciences &Humanities Open 2 (2020) 100017
3
PPR readers showed connectivity between the left posterior reading
systems and right prefrontal areas often associated with working memory
and memory retrieval.
In Fig. 3, we can see a suggestive image of how interconnection be-
tween the areas of the brain works.
The image, produced from the observation of a living brain, shows the
constant intercommunication between the various parts of the brain,
characteristic of brain activity. The lines, in figurative colors, indicate the
direction of the movements of said activity and suggest the connection
between the various parts of the brain in information processing. This
intense activity continually enlarges and modifies the brain itself and its
ability to learn.
The ability to change from experience at structural, functional and
morphological levels is called cerebral plasticity (Hebb, 1949;Kandel,
Schwartz, Jessel, Siegelbaim, &Hudspeth, 2013;Kolb &Whishaw,
1998). Although the foundations for the concept of brain plasticity were
first mentioned in the 19th century by James (1899, p. 135) when he
spoke of the “organic matter, especially nervous tissue, seems endowed
with a very extraordinary degree of plasticity”, it was technological
development which allowed Neuroscience to observe, through images,
changes in the brain structure, in the neurons and in the interconnectivity
between areas of the brain (Rees, Booth, &Jones, 2016).
The changes in brain structure derived from the ageing of the or-
ganism in childhood and adolescence, known as sensitive periods
(Knowland &Thomas, 2014), were noted in various studies. Giedd et al.
(1999) suggested, from a longitudinal study they conducted, that the
volume of the brain only reaches its peak at the age of 14.5 years for boys
and 11.5 years for girls. Other studies indicate that the volume of grey
matter mass and of white matter mass changes during childhood (Lenroot
&Giedd, 2006;Schmithorst &Yuan, 2010) and increases during the shift
from to adolescence and adulthood (Giorgio et al., 2010).
Nevertheless, other than in the sensitive periods, the brain ability to
change depending on the environment continue throughout life, albeit
less intensely intensidade (Knowland &Thomas, 2014). In a study con-
ducted by Draganski et al. (2004) on young adults, a significant increase
in the volume of grey mass was observed in both brain hemispheres in
areas that connect sight and motor control after three months of juggling
practice. Also, after three months of the subject not training, the volume
of that area went back to the initial level. Dehaene et al. (2010a,b)
observed the brain activity of three groups of subjects: literate since
childhood, illiterate and literate in adulthood. The researchers noted that
a brain area related to image recognition (VWFA) was more active in the
illiterate and the literate in adulthood than in the literate since child-
hood. Both studies are suggestive of the impact that learning a new skill –
be it motor or cognitive –can have in changing the brain structure.
6. Conclusion
In this paper, we present the concept of learning styles and their
theoretical foundations based on the theory of experiential learning by
Kolb (1984). Among the many instruments for identifying learning styles,
we chose to present LSI (Kolb, 1971,1976), LSQ (Honey &Mumford,
1992), ILS (Felder &Silverman, 1988) and VARK (Fleming, 2001) due to
their similarity in the conception of learning and the theoretical para-
digm that supports them.
Also, we have established a link between the learning styles and the
Fig. 2. Motor and Sensory Regions of the Cerebral Cortex.
Source: Blausen.com staff (2014). “Medical gallery of Blausen Medical 2014”. WikiJournal of Medicine 1 (2).
Fig. 3. Interconnections in the brain.
Source: Dam
asio, H. (s.d.) cited by Dam
asio, A. (2019).
L.A. Dantas, A. Cunha Social Sciences &Humanities Open 2 (2020) 100017
4
knowledge coming from Biology and the Neurosciences, in order to
broaden the debate on the subject. From the theories and models pre-
sented, we draw some considerations that can contribute to the debate
about the topic among teachers.
1. In order to learn, we use different external channels, through which
we capture information, which is then processed internally in artic-
ulation with the knowledge we already have, the environment and
the time in which we live. Although they are not discussed in this text,
we add here the widely known psychological and affective issues
involved in learning.
2. With this we highlight the individuality of the process and the paths
chosen by each subject in the construction of knowledge. For stu-
dents, knowing their learning style can help them make learning more
attractive by prioritizing how they organize their activities and the
types of input they are more stimulated by. For teachers, recognizing
that there are different ways of learning favors a change in the very
conception of learning and the traditional model of education, which
is almost always rooted in an approach to didactics that prioritizes
visual and auditory information.
3. The knowledge that we offer today, coming from the constructivist
and socio-interactionist learning theories, articulated with new de-
velopments in Biology and the Neurosciences, indicate that learning
happens through the continuous interaction of endogenous and
exogenous factors. The process changes, because it changes the
accumulated knowledge and, with it, individuals’own learning
ability and strategies.
Based on this analysis, it seems therefore restrictive to choose a single
learning style, in other words, to focus on a single type of stimulus for the
organization of learning activities as a presupposition for better learning.
While recognizing individuality and the preferences of the subject,
providing students with different stimuli, equivalent to the various styles
explained here, will constitute the most appropriate methodology to the
construction of learning.
CRediT authorship contribution statement
Lucimar Almeida Dantas: Conceptualization, Methodology, Writing
- original draft. Ana Cunha: Conceptualization, Writing - review &
editing.
References
Arslan, O. E. (2016). Computational basis of neural elements. In P. Munish, P. Yashwant,
K. S. Vijay, T. Srinivas, & M. Wilfrido (Eds.), Artificial neural network for drug design,
delivery and disposition. Elsevier.
Awla, H. A. (2014). Learning styles and their relation to teaching styles. International
Journal of Language and Linguistics, 2(3), 241–245.
Cassidy, S. (2004). Learning Styles: An overview of theory, models, and measures.
Educational Psychology, 24(4), 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: Learning and Skills Research
Centre.
Dehaene, S., Pegado, F., Braga, L. W., Ventura, P., Nunes Filho, G., Jobert, A., et al.
(2010). How learning to read changes the cortical networks for vision and language.
Science, 330(6009), 1359–1364.
Dehaene, S., Spelke, E., Pinel, P., Stanescu, R., & Tsivkin, S. (1999). Sources of
mathematical thinking: Behavioral and brain-imaging evidence. Science, 284,
970–974.
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004).
Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972),
311–312.
Dunn, R. (1990). Rita Dunn answers questions on learning styles. Educational Leadership,
48,15–19.
Felder, R. M., & Silverman, L. K. (1988). Learning styles and teaching styles in
engineering education. International Journal of Engineering Education, Ontario, 78(7),
674–681.
Felder, R. M., & Soloman, B. A. (1991). Index of learning styles questionnaire. North
Carolina State University, 1991. Disponível em http://www4.ncsu.edu/unity/locke
rs/users/f/felder/public/ILSdir/styles.htm.
Fleming, N. D. (2001). Teaching and learning styles: VARK strategies. Christchurch, New
Zeland: N.D. Fleming.
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., et al.
(1999). Brain development during childhood and adolescence: A longitudinal MRI
study. Nature Neuroscience, 2(10), 861–863.
Giorgio, A., Watkins, K. E., Chadwick, M., James, S., Winmill, L., Douaud, G., et al.
(2010). Longitudinal changes in grey and white matter during adolescence.
NeuroImage, 49(1), 94–103.
Hebb, D. O. (1949). The organization of behavior. New York, NY: Wiley.
Honey, P., & Mumford, A. (1992). The manual of learning styles. Maidenhead, England:
Peter Honey Publications.
James, W. (1899). Talks to teachers on psychology—and to students on some of life’s ideals.
Metropolitan Books/Henry Holt and Company.
James, W. B., & Gardner, D. L. (1995). Learning styles: Implications for distance learning.
New Directions for Adult and Continuing Education, 67,19–32.
Kandel, E., Schwartz, J., Jesell, T., Siegelbaim, S. A., & Hudspeth, A. J. (Eds.). (2013).
Principles of neural science (5th ed.). E.E.U.U.: McGraw Hill.
Kara, S. (2009). Learning styles and teaching styles: A case study in foreign language
classroom. Conference of the International Journal of Arts and Sciences, 1(20), 77–82
(2009) CD-ROM. ISSN: 1943-6114.
Kazu, I. Y. (2009). The effect of learning styles on education and the teaching process.
Journal of Social Sciences, 5/2,85–94.
Knowland, V. C. P., & Thomas, M. S. C. (2014). Educating the adult brain: How the
neuroscience of learning can inform educational policy. International Review of
Education, 60(1), 99–122.
Kolb, D. A. (1971). Individual learning styles and the learning process. Sloan School of
Management, Massachusetts Institute of Technology. Working Paper #535-71.
Kolb, D. A. (1976). Learning style inventory. Boston, MA: McBer &Company.
Kolb, D. (1984). Experiential learning. New Jersey: Prentice Hall Inc.
Kolb, D. A. (1985). Learning style inventory (Revised Edition). Boston, MA: McBer &
Company.
Kolb, D. A. (1999). Learning style inventory (p. 116). Boston, MA: Hay Resources Direct.
Huntington Avenue, Boston, MA 02116 Version 3. trg_mcber@haygroup.com.
Kolb, D. A., & Kolb, A. Y. (2013). The Kolb learning style inventory - version 4.0. A
comprehensive guide to the theory, psychometrics, research on validity and educational
applications.
Kolb, B., & Whishaw, I. Q. (1998). Brain plasticity and behavior. Annual Review of
Psychology, 49(1), 43–64.
Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents:
Insights from anatomical magnetic resonance imaging. Neuroscience &Biobehavioral
Reviews, 30(6), 718–729.
Massa, L. J., & Mayer, R. E. (2006). Testing the ATI hypothesis: Should multimedia
instruction accommodate verbalizer-visualizer cognitive style? Learning and Individual
Differences, 16, 321–336.
Naimie, Z., Siraj, S., Piaw, C. Y., Shagholi, R., & Abuzaid, R. A. (2010). Do you think your
match is made in heaven? Teaching styles/learning styles match and mismatch
revisited. Procedia Social Behavioral Sciences, 2, 349–353.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles concepts and
evidence. Psychological Science in the Public Interest, 9,3.
Rees, P., Booth, R., & Jones, A. (2016). The emergence of neuroscientific evidence on
brain plasticity: Implications for educational practice. Educational and Child
Psychology, 33(1), 8–19.
Reid, J. M. (1995). Learning styles in the ESL/EFL classroom. Boston: Heinle &Heinle
Publishers.
Schmithorst, V. J., & Yuan, W. (2010). White matter development during adolescence as
shown by diffusion MRI. Brain and Cognition, 72(1), 16–25.
Schmitt, C. S., & Domingues, M. J. C. S. (2016). Estilos de aprendizagem: Um estudo
comparativo. Avaliaç~
ao, Campinas; Sorocaba, SP, 21(2), 361–385.
Scott, C. (2010). The enduring appeal of ,learning styles’.Australian Journal of Education,
54/1,5–17.
Shaywitz, S. E., & Shaywitz, B. A. (2005). Dyslexia (specific reading disability). Biological
Psychiatry, 57, 1301–1309.
Tuan, L. T. (2011). Matching and stretching learners’learning styles. Journal of Language
Teaching and Research, 2(2), 285–294.
L.A. Dantas, A. Cunha Social Sciences &Humanities Open 2 (2020) 100017
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