Advances in the Use of
Neuroscience Methods in Research on Learning and Instruction
Bert De Smedta
a
Faculty of Psychology and Educational Sciences, University of
Leuven, Belgium
Article received 27
May 2014 / revised 19 September 2014 / accepted 11
November 2014 / available 23 December 2014
Abstract
Cognitive
neuroscience offers a series of tools and methodologies that
allow researchers in the field of learning and instruction to
complement and extend the knowledge they have accumulated
through decades of behavioral research. The appropriateness of
these methods depends on the research question at hand.
Cognitive neuroscience methods allow researchers to
investigate specific cognitive processes in a very detailed
way, a goal in some but not all fields of the learning
sciences. This value added will be illustrated in three ways,
with examples in field of mathematics learning. Firstly,
cognitive neuroscience methods allow one to understand
learning at the biological level. Secondly, these methods can
help to measure processes that are difficult to access by
means of behavioral techniques. Finally, and more indirectly,
neuroimaging data can be used as an input for research on
learning and instruction. This paper concludes with
highlighting the challenges of applying neuroscience methods
to research on learning and instruction.
Frontline:
Cognitive neuroscience offers a series of tools and
methodologies that allow researchers in the field of learning
and instruction to complement and extend the knowledge they
have accumulated through decades of behavioral research. The
appropriateness of these methods depends on the research
question at hand.
Keywords: Cognitive Neuroscience; Methods; Mathematics
Learning; Educational Neuroscience
1.
Introduction
Non-invasive brain imaging
methods, such as Event-Related Potentials (ERP) or functional
Magnetic Resonance Imaging (fRMI), represent a series of tools
and methodologies that allow researchers in the field of
learning and instruction to complement and extend the knowledge
they have already accumulated through decades of behavioral
research (e.g., Cacioppo, Berntson, & Nusbaum, 2008; De
Smedt, Ansari, et al., 2011). The potential application of these
methods depends on the research question at hand. After a brief
discussion of relevant brain imaging methods, I will use the
field of mathematics learning to illustrate three ways in which
these methods can be used in research on learning and
instruction. This paper concludes with highlighting some
challenges of applying such methods to research on learning and
instruction.
2.
Neuroscience methods
When considering different
methods that are used by neuroscientists to study the structure
and function of the brain, it is important to point out that
neuroscience is a very broad field that includes a variety of
disciplines ranging from cellular and molecular neuroscience to
cognitive neuroscience (e.g., Squire et al., 2013). I restrict
the focus here to cognitive neuroscience and its methods (Ward,
2006), because this sub-field of neuroscience is the closest to
research on learning and instruction, given its focus on the
neural mechanisms that underlie human cognition and behavior. A
detailed description of these cognitive neuroscience methods is
beyond the scope of this contribution and excellent
introductions are provided by Ward (2006) and Dick, Lloyd-Fox,
Blasi, Elwell, & Mills (2014). Sometimes,
psychophysiological measures, such as skin conductance, heart
rate or eye-movement data, are also denoted as neuroscience
methods. Although these methods tap into the nervous system,
they are not direct measures of brain structure or function and
therefore they are not considered here.
The transmission of
information in the brain from one cell to the other occurs
through electrical signals, and this electrical activity of the
brain can be captured by methods such as electroencephalography
(EEG), which requires a cap of electrodes to be mounted on the
head of a participant, and magnetoencephalography (MEG) (Ward,
2006). On one hand, the advantage of these methods is that they
can measure the activity of the brain in response to a
particular stimulus (i.e. event-related activity) at a very
accurate temporal scale and they are particularly suited to
investigate when a process is taking place. On the other hand, a
large number of stimuli of a particular type (typically a few
dozens) are needed in order to reliably estimate the brain
signal in response to that stimulus.
Another series of methods
are magnetic resonance imaging (MRI) techniques, which use large
magnetic fields and the magnetic properties of hydrogen atoms in
brain tissue or in blood to visualize brain structure and brain
function, respectively (Ward, 2006). These data are acquired in
a specific and very noisy environment, the MRI scanner, in which
participants have to lie still and are not allowed to move more
than a few millimeters. This category of methods can investigate
the structure of the brain, i.e. the gray or white matter, and
how this structure is related to performance or changes as a
result of learning. Interesting examples are provided by Supekar
et al. (2013), who showed that the size of the hippocampus
predicted the performance gains in response to one-on-one math
tutoring and by Keller and Just (2009), who showed that
intensive remedial reading instruction resulted in changes in
white matter in poor readers.
MRI also allows us to
investigate brain function, a technique that is called
functional MRI or fMRI, which is one of the most common
techniques used in cognitive neuroscience (Ward, 2006).
Functional MRI is an indirect way of assessing the brain’s
activity and measures the level of oxygen in the blood. The
assumption is that an increase in oxygen level is the result of
the vascular system’s response to an increase in brain activity.
MRI methods are very accurate on a spatial scale and are
particularly suited to investigate where in the brain a
particular process is taking place. Due to the practical
constraints of the MRI-environment (e.g., noise, no movement)
the type of tasks that participants can complete is limited, yet
progress is being made over the last years to use more complex
tasks, such as playing video games (Anderson et al., 2011) and
even face-to-face interaction (e.g., Redcay et al., 2010).
It is crucial to point out
that the measures reviewed above, i.e. signals indicating brain
structure or function, can only be meaningfully interpreted by
linking them to cognitive theories (e.g., Cacioppo et al., 2008;
De Smedt, Ansari, et al., 2011). Furthermore, the collection of
behavioral data represents a necessary step in most studies in
cognitive neuroscience (e.g., Ward, 2006). In all, a detailed
cognitive theory of the phenomenon under investigation is
crucial to design and interpret cognitive neuroscience data and
to apply cognitive neuroscience methods to the field of learning
and instruction.
3.
Application to research on learning and
instruction
How can the cognitive
neuroscience methods reviewed above advance the field of
learning and instruction? This depends on the research question
at hand, and only some but certainly not all types of research
questions in the field of learning and instruction might benefit
from the use of cognitive neuroscience methods. Stern and
Schneider (2010) provided a nice analogy for determining when
these cognitive neuroscience tools and theories could be
appropriate. They compared this issue with the use of a digital
road map. When using a digital road map for looking at the field
learning and instruction, the appropriate resolution of the map
depends on what the map viewer is looking for, alleys
(micro-level) vs. highways (macro-level), and users can zoom in
and out between different levels of resolution. Some questions
only focus at the broader context of learning (macro-level), as
is the case in large-scale research on educational systems, and
are at a low level of resolution. Others aim to unravel the very
specific cognitive processes that underlie learning, and this
requires a map at very high resolution (micro-level). It is at
this micro-level of understanding of such specific cognitive
processes that cognitive neuroscience methods can be applied in
the field of learning and instruction. I will use the field of
mathematics learning to illustrate three ways in which cognitive
neuroscience methods can be useful for research in learning and
instruction (see also De Smedt et al., 2010; De Smedt, Ansari,
et al. 2011; De Smedt & Grabner, 2015).
3.1 Understanding
learning
at the biological level
Neuroimaging data allow us
to examine at the biological level how people learn. Such data
can provide converging evidence for findings that have been
obtained through psychological and educational research. This
convergence of findings from different research methodologies
has the potential to provide a better and more complete
understanding how typical and atypical learning takes place
(e.g., De Smedt, Ansari, et al., 2011; Lieberman, Schreiber,
& Ochsner, 2003). For example, how do people acquire and
apply different strategies to solve elementary arithmetic
problems, such as 5 + 9 or 4 × 3? Decades of behavioral research
have revealed that these problems are either solved by using
fact retrieval from declarative memory or by using procedural
strategies, such as counting, and developmental data indicate
that children develop an increasing reliance on arithmetic fact
retrieval, while the use of procedures to solve such elementary
problems decreases over time (e.g., Siegler, 1996). Research in
cognitive neuroscience is now beginning to understand on how
this learning of arithmetic is reflected at the neural level
(e.g., Arsiladou & Taylor, 2011; Zamarian, Ischebeck, &
Delazer, 2009).
In a series of studies, we
have tried to investigate this issue with EEG (De Smedt,
Grabner, & Studer, 2009; Grabner & De Smedt, 2011;
Grabner & De Smedt, 2012). In these studies, adults had to
solve a series of addition, subtraction and multiplication
problems, while their brain activity was recorded with EEG, and
they had to verbally report on a trial-by-trial basis on the
strategies they used to solve the presented problems. These
studies had two aims. First, we wanted to verify whether these
two types of strategies were reflected in different brain
activity patterns and whether fact retrieval training resulted
in changes in brain activity that reflected a shift in strategy
use. Second, we aimed to test if cognitive neuroscience methods,
such as EEG, can be used as a way of methodological
triangulation to further validate the use of verbal report data.
These data are typically used in behavioral research to
investigate strategy use but their validity has been debated
(e.g., Kirk & Ashcraft, 2001).
The EEG data revealed
different patterns of activity for the two types of strategies:
oscillations in the theta band (3–6 Hz) were associated with
fact retrieval whereas oscillations in the lower alpha band
(8–10 Hz) were related to procedural strategies (Grabner &
De Smedt, 2011). When we trained participants in using fact
retrieval strategies, we were also able to show that the
well-known behavioral shift from procedural strategies to fact
retrieval as a function of training was also reflected in
specific changes in brain activity, i.e. training-related
activity increases in the theta band and decreases in the lower
alpha band (Grabner & De Smedt, 2012). Combining verbal
strategy reports with reaction times on specific problem types
and neuroimaging data allowed us to further examine the validity
of these verbal reports. This type of methodological
triangulation confirmed that verbal strategy reports are a valid
way to capture strategies in mental arithmetic. In all, this
convergence of findings obtained by different research methods
at behavioral and biological levels provides a more solid
empirical ground for our theories on strategy development.
3.2 Measuring
difficult-to-access
processes
Neuroimaging data can
provide a level of analysis and measurement that cannot be
accessed by behavioral data alone. Examples of this application
can be observed in the study of individual differences between
learners and in understanding the origins of atypical
development. De Smedt, Holloway, & Ansari (2011) used fMRI
to investigate brain activity in 10-12-year-old children during
addition and subtraction and compared children with low and
average levels of arithmetical competence, who significantly
differed in their performance on a standardized arithmetic
fluency test. Although both groups of children did not differ in
a simple calculation task at the behavioral level (i.e.
accuracy, speed) during the acquisition of the fMRI data, the
authors observed significant group differences in brain activity
in the right intraparietal sulcus, a brain region that is known
to play a key role in the processing of numerical magnitudes:
Children with low levels of arithmetical competence showed
higher activation in this region during the solutions of
problems with a relatively small problem size. The
interpretation of these data in the context of neurocognitive
theories of numerical magnitude processing and arithmetic
development (e.g., Ansari, 2008; Butterworth, Varma, &
Laurillard 2011) suggests the use compensatory strategies and
generates predictions that should be further exploited in
subsequent research. For example, it might be that the children
with low arithmetical competence in the study of De Smedt,
Holloway, et al. (2011) continued to rely to a greater extent on
quantity-based strategies (such as counting or procedural
calculation) on those problems that children with relatively
higher arithmetical competence already retrieved from their
memory, a possibility that should be evaluated in subsequent
research. In all, this indicates that brain imaging data can
uncover subtle processing differences between groups of learners
that may not be detected through the measurement of behavioral
data alone, illustrating the high resolution level which
cognitive neuroscience methods are able to capture.
3.3 Input
for
research on learning and instruction
Studies in cognitive
neuroscience can also have an indirect impact on research in
learning and instruction, by drawing our attention to specific
fine-grained cognitive processes that are implicated in
different types of learning (see Aue, Lavelle, & Cacioppo,
2009 for a similar rationale in the field of psychology). Such
data have the potential to generate new hypotheses that can be
tested in research on learning and instruction.
For example, neuroimaging
studies on how the brain processes numbers have revealed that
the intraparietal sulci (IPS) are consistently active whenever
we have to perform numerical and arithmetical tasks and that
this structure supports the processing of numerical magnitudes
(e.g., Ansari, 2008; Dehaene, Piazza, Pinel, & Cohen, 2003).
Brain imaging studies in children with developmental
dyscalculia, a learning disorder that is characterized by severe
and persistent difficulties in acquiring mathematical
competencies, point to structural and functional abnormalities
in the IPS in these children (e.g., Butterworth et al., 2011;
Price & Ansari, 2013 for a review). This all suggests that
the processing of numerical magnitudes is potentially a key to
successful mathematical development and this processing might be
compromised in developmental dyscalculia (DD). This suggestion
has fueled a large number of psychological and educational
studies that have empirically confirmed this hypothesis at the
behavioral level (see De Smedt, Noël, Gilmore, & Ansari, for
a review), by consistently showing that individuals with DD have
significant impairments in their ability to compare (symbolic)
numbers. More broadly, these studies have also furthered our
understanding of individual differences in typical mathematical
development, as the ability to compare (symbolic) numbers is
predictive of subsequent mathematical development (see De Smedt
et al., 2013, for a review). This research has impacted on
studies in the field of learning and instruction, through the
development and evaluation of specific interventions (e.g., De
Smedt et al., 2013) and diagnostic instruments that can be used
for the screening and early identification of at-risk children
(Nosworthy, Bugden, Archibald, Evans, & Ansari, 2013). It is
important to point out that even if these studies do not collect
measures of brain activity or structure, they rely to some
extent on insights gleaned from cognitive neuroscience studies.
Used in this way, cognitive neuroscience data might set the
stage for new educational research and it can, albeit
indirectly, enhance our understanding of learning.
4.
Challenges
The application of
cognitive neuroscience methods to research on learning and
instruction also imposes some challenges and caveats that one
needs to be aware of (see also Ansari, De Smedt, & Grabner,
2012; De Smedt & Grabner, 2015), which are not specific to
the domain of mathematics learning. These challenges deal with
the issue of external validity or generalizability as well as
the scope of biological data and explanations (e.g., Beck,
2010).
It is important to point
out that most of the existing studies in cognitive neuroscience
involved adult participants and that these methods are not so
easy to apply in children. This is because the acquisition of
neuroimaging data is very sensitive to movement and motion
artefacts in children often negatively impact on data
acquisition. Progress is being made in the reduction of such
artefacts, for example by training children to keep still when
such data are being collected (de Bie et al., 2010). At a more
theoretical level, cognitive neuroscience findings obtained in
adult participants cannot be readily generalized to the
developing brain and the learning of children and adolescents,
as the human brain undergoes massive structural and functional
changes throughout childhood and adolescence (Ansari, 2010).
The tasks used in most
cognitive neuroscience studies are very elementary and differ
from the rich and complex tasks that are typically solved in
everyday learning environments and that are used in research
learning and instruction. Such complex tasks cannot be easily
administered in cognitive neuroscience studies for various
reasons. As indicated above, there are practical constraints
related to the laboratory environment in which neuroimaging data
are being collected. In order to obtain reliable data on brain
activity during a particular task, a large number of trials of
the same task need to be presented. These tasks need to be very
elementary, because the larger the number of cognitive processes
in a particular task, the more difficult it will be to
disentangle these cognitive processes physiologically. One way
to resolve this is to correlate data acquired in very
constrained laboratory settings to ecologically valid measures
of learning (see Price, Mazzocco, & Ansari (2013), for an
example).
One important caveat deals
with the scope of a neuroscientific data and explanations. There
might be an inappropriate belief that neuroscientific data are
more convincing, informative and valid than behavioral data
(Beck, 2010). On the contrary, knowledge gained through
cognitive neuroscience methods should be considered at the same
level of data obtained by standard behavioral methods in
learning and instruction. There should be no knowledge
hierarchy, but an appreciation of multiple sources of data to
better understand how learning takes place and how it can be
fostered (De Smedt, Ansari, et al., 2011).
5.
Conclusion
The application of
neuroscience methods to research on learning and instruction
depends on the level of the research question. When interested
in very specific low-level processes, neuroimaging data have the
potential to help understanding learning at the biological
level, to measure processes that are difficult to access via
behavioral data and to generate and test hypotheses for
educational phenomena that can be subsequently investigated via
behavioral research on learning and instruction.
Keypoints
Acknowledgments
This work is partially supported by grant GOA
2012/010.
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