Perspectives
on Learning:
Methodologies
for Exploring Learning Processes and Outcomes
Susan R. Goldman
Learning Sciences
Research Institute
University
of Illinois, Chicago, USA
Article received 27
May 2014 / accepted 2 December 2014 / available
online 23 December 2014
Abstract
The papers in this
Special Issue were initially prepared for an EARLI 2013
Symposium that was
designed to examine methodologies in use by researchers from
two sister
communities, Learning and Instruction and Learning Sciences.
The four papers
reflect a common ground in advances in conceptions of learning
since the early
days of the “cognitive revolution” in the 1960s. This
commentary shows the
interdependence between advances in theory and advances in
methodologies. Four
shifts in conceptions of learning are described. That these
shifts are evident
in the work of both communities suggests a blurring of the
boundaries between
the two.
During the 30 year period
from
1960 – 1990, the majority of studies of learning took place in
one location:
the laboratory; looked at individual cognition as a function of
an
operationally defined and restricted set of variables, in one
time frame with
occasional return visits. Although some researchers were
examining learning in
the context of tasks students might be asked to do in school,
many of the
“learning” situations were set up as experiments that were
highly constrained
to maintain experimental control over “extraneous” variables; as
well the tasks
were frequently “toy” problems that had little relevance to
classrooms or other
contexts outside of cognitive theory and the academic settings
in which the
research was being conducted. Consequently, it was difficult to
see how
findings from the lab could possibly have relevance to everyday
learning.
Emphases were on manipulating characteristics of the materials,
the task, or
both and observing how people “solved” the tasks and their
success at doing so
with some emphasis on understanding how they had completed the
tasks. Some of
the findings emerging from that research shaped instructional
studies in which
people were instructed to summarize sections of text, underline
main ideas,
break down tasks into subtasks before solving them, group words
based on
taxonomic categories to improve memory, and similar heuristics.
Published
studies of this sort attest to the success of these approaches
in building a
cognitive theory of learning and problem solving that superceded
extant behaviourist/empiricist
views (cf. Greeno, Collins, & Resnick, 1996). There was,
however, little
uptake of these theories by educational practitioners.
In addition to changes in
the
conceptualization of learning and the kinds of questions being
asked about
learning, data analytic methodologies have come a long way since
the 1960s.
Some of the (older) readers of this article will remember the
days of doing
ANOVAs by hand, with “advances” marked by programmable Wang
calculators, and
main frame programs that automated the process. Of course, you
had to batch
process jobs, submitting stacks of punch cards containing the
data (all the
time living in fear that you would drop the deck and have to
start again making
sure they were in the right order) and then wait for the print
out. Turnaround
varied from 10 minutes to 24 hours. Over the past 30 years there
have been huge
advances in the technologies and data analytic applications
available on
devices that are small enough to carry around the way we once
carried pads of
paper and notebooks (not electronic ones). These technologies
have expanded the
ways we dare to think about analyzing our data, enabled us to
collect and make
sense of new forms of data, and automated or semi-automated
analysis methods
that we used to do solely by hand.
Currently, the Learning
Sciences
and Learning and Instruction communities operate with
theoretical frameworks on
learning that reflect more complex views of learning in four
major ways. We now
understand and attempt to study learning
1.
in
multiple and iteratively designed environments
2.
over
multiple time scales
3.
occurring
in social groups of multiple and collaborating individuals
4.
with
effects evident at multiple levels ranging from behavioural to
neural.
As a set, the papers in
this
Special Issue reflect these shifts in conceptions of learning
and its
investigation and provide us with methodological tools that
enable us to
rigorously investigate learning processes and outcomes despite
the greater
complexity of doing so. There is evidence of one or more of
these shifts in
conceptions of learning among researchers who identify with
Learning Sciences,
as well as among those who identify with Learning and
Instruction, suggesting
something of a convergence, or at least a blurring of the
boundaries of the two
communities.
1.
Learning in Multiple and
Iteratively Designed Environments
Design-based research
marked a
pivotal shift in perspectives on learning and its study in
classrooms. Svihla
(this issue) provided an excellent description of the goals of
this research
approach. Up until
the time that this
approach to research on learning was introduced in the early 90s
by Ann Brown
(1992) and Allan Collins (1992), educational research in schools
typically took
the form of relatively short-term experiments that involved
comparisons of the
effects of different methods or materials on various cognitive
skills. The
studies were usually conducted by the researchers. Teachers
“cooperated” with
the researchers in terms of providing access to their students
for the duration
of the study but were otherwise minimally involved in
contributing to the
instructional design or the materials. A major goal of this
research was ascertaining
which instructional methods were better than others for
achieving largely
cognitive objectives such as more accurate mathematics
performance, better
memory for new vocabulary, and better comprehension of text.
Accordingly,
assessments were designed to measure changes in students’
performance as a
function of having participated in the study either in the
“experimental
treatment” or the “control” group. Along with these types of
cognitive studies
there were similarly designed studies that examined the impact
on individuals
of having worked in cooperative groups (e.g., Johnson &
Johnson, 1999; see
for review Webb & Palincsar, 1996).
As Svihla described, the
goals of
DBR reflected a fundamental shift to an emphasis on studying
learning processes
in situ as both social and interactional (Collins, 1992).
Learning processes
were studied in the context of designed learning environments
developed through
collaborations of researchers and practitioners and based on
principles that
constituted a learning theory. Enactments of designs were
objects of study for
purposes of understanding how, with the understandings that
emerged from close
study of, and reflection on, the interactions and student work
informing
iterative refinement of the learning theory principles, and
designs. Svihla
does an excellent job of depicting the ways in which DBR has
developed since
its initial introduction. Suffice to say it made apparent the
need for
methodologies to capture processes occurring over multiple time
scales and
among individuals in social configurations.
2.
Learning processes over
multiple time scales
Complex views of learning
make it
clear that processes occur over time, with different learning
processes
occurring at different time scales. Molenaar (this issue)
provided an excellent
rationale for the need for temporal analysis methods. She
described a variety
of the issues involved in shifting from a focus on whether a
particular
construct has been learned or not to a focus on how that
construct is learned,
what that learning looks like at different time scales, and
indeed what
constructs are conceptualized as emerging over longer versus
shorter time
frames (cf. Lemke, 2000). She referenced a variety of constructs
that we now
think of as emerging over events and across time but that used
to be thought of
as personality traits (e.g., motivation, persistence). She
discussed various
computational tools that can aid in segmenting, coding, and
relating different
time scales. These methodologies are critical to doing the
analyses needed to
understand learning over different time scales.
A variety of issues face us
as
individual researchers and as a community as we apply
methodologies for
temporal analysis: What units of time are appropriate for
particular constructs
of interest, especially when multiple time scales operate in
parallel? How do
we determine the time scale most appropriate for tracing the
emergence of a
construct over time? Or alternatively, how do we capture the
interrelationships
between events occurring over time but at different time scales?
Of potentially
many patterns of events that might be extracted by pattern
detection software,
how do we determine which are psychologically meaningful and at
what scale of
time they are meaningful?
Equally necessary are new
forms
of representation that can assist us in conveying our findings
to the broader
community. Molenaar (this issue) presented one form of
representation. Figure 1
illustrates a different form of representation in which we
plotted the
discourse moves of three students comprising a small group
engaged in a science
investigation (Radinsky, Goldman, Doherty, & Ping, 2010).
This particular
figure shows the moves for the first day of the investigation.
We plotted
similar representations for each day and then used the graphs to
identify
regions where there were clusters of moves across the three
students that
suggested there were interesting dialogic discourses occurring.
We then “dove”
into these segments of the discourse to determine the character
of the “argumentation”
in which the students were engaged and whether the claims and
evidence being
offered were similar or different later in the investigation
versus earlier. As
well, we considered how participation and roles in the discourse
revealed
dimensions of identity and positioning with respect to
disciplinary competence.
This form of representation was a useful analytic tool and with
some refinement
might be a useful way to represent the time course of argument
development
(Radinsky, et al., 2010).
Molenaar highlighted the
need to
conceptualize different dimensions of time in order to define
important
temporal characteristics. She cited papers by Bloome, et al.
(2009) and Lemke
(2000) as informing this discussion. In addition, Bahktin’s
(1981) framings of
time in relation to discourse, meaning, and learning will be a
useful resource.
We also need to (re)connect learning and development. Indeed,
the move from the
more traditional educational research paradigms to DBR and
related learning
sciences methodologies creates a convergence between research on
development
and research on learning. That is, one distinction between
development and
learning had traditionally been the time frame over which
phenomena of interest
emerged. Those that occurred over multiple years were called
developmental,
e.g., oral language; those over minutes or hours, learning,
e.g., declarative
knowledge such as “c – a – t”
spells
cat; or propositions such as EARLI is a professional research
organization.
Arguably, a second distinction was whether the phenomenon
emerged with or
without formal instruction, the latter being deemed
developmental phenomena and
the former learning. For example, children develop oral language
but learn to
read with explicit instruction. Because developmental
psychologists have long
been concerned with the study of change over time, they have
developed
techniques that examine change over relatively longer periods of
times such as
growth analysis (Willett,
1989), as well
as techniques that look at moment - to - moment change, such as
sequential
analysis (Bakeman & Quera, 1995) and microgenetic analysis
(e.g., Kuhn,
1995; Siegler & Stern, 1998). These methods are rich
resources for
examining learning over multiple time scales.
3.
Learning in social
groups of multiple and collaborating individuals
A core assumption of the
Learning
Sciences is that learning is social and interactional and takes
place through
situated activity (Brown, Collins, & Duguid, 1989). Collins,
Brown, and Newman
(1989) labeled the approach cognitive apprenticeship, reflecting
the importance
of observing the habits of mind as well as the actions of the
more
knowledgeable others in the community (cf. Vygotsky, 1978).
Hence, discourse
about activity and interaction with others engaged in the
activity became a
central focus for understanding learning. Researchers with
intellectual roots
in a variety of disciplines have long relied on discourse among
participants in
a joint activity as a window into knowledge building processes
of groups as
well as individuals, and, along with gestures, into processes of
learning
through joint activity (Gee, 1992; Goodwin, 1994; Hutchins,
1995; Lave &
Wenger, 1991; Sawyer, 2006; Scardamalia & Bereiter, 1991;
Schegloff, 1991,
2007). Video and audio recordings have typically provided the
raw data and
various types of very time-consuming and intensive qualitative
analyses have
been used by researchers to provide evidence for claims about
learning outcomes
and processes. Frequently and understandably given the
labor-intensive nature
of these analyses, the evidence provided in any one empirical
report has tended
to be based on relatively small data sets or corpora.
Many computer-supported
collaborative learning (CSCL) environments make available
written traces of
learning interactions that can also be mined to understand
learning processes
and outcomes for individuals and for groups. Although initially
these were also
analyzed by humans using processes similar to those used for
coding discourse
that was transcribed from video and audio recordings, a number
of
computer-assisted methods have been developed that make the work
of coding less
time-consuming. Stegmann (this issue) argued that to understand
the mechanisms
that produced enhanced learning outcomes in CSCL, three
hypotheses needed to be
tested. These have been conceptualized as a “triangle of
hypotheses:” “(a)
instructional/technological support facilitates learning
activities; (b)
facilitated learning activities have positive effects on
learning outcomes; and
(c) mediated by learning activities, instructional/technological
support has a
positive effect on learning outcomes.” (Stegmann, this issue, p.
#, citing
Wecker, Stegmann & Fischer, 2013; Fig. 1). It could be
argued that these three
hypotheses can be thought of as constituting an activity system
(Engeström,
1987, 2001) in which tools (technology support), activities, and
performances
of individuals and groups exist in interaction with one another
and over time.
Stegman and colleagues argue that conceptualizations other than
experimental
designs are needed to establish relationships between learning
tools,
activities, and outcomes. They propose the use of nomological
nets to ensure
that direct and mediating relationships between the tools and
outcomes can be
tested. Nomological nets specify what constructs are indexed to
which
observables over what time frames. As well interrelationships
among constructs
are specified. Empirical evidence derived from collaborative
activities constitute
input to revisions and refinements of theoretically grounded
nomological nets.
These revisions may reflect mediational variables that become
evident through
indepth analyses of the discourse, of changes in the
interactions and discourse
over time, and at different ”units” of analysis (e.g.,
individual, dyad, small
groups, entire activity system).
Stegmann (this issue)
argued that
the indepth analyses required to “test” initially specified
nomological
nets should
take advantage of
statistical techniques designed to detect patterns of
interactions as they
occur over time. These
techniques
require some form of quantified information; therefore,
qualitative analyses
need to be quantified. Fortunately, there are a number of
computational
algorithms that can assist researchers in doing so. Importantly,
these systems
assist researchers in parsing the input as well as counting
instances of
particular codes and discovering repeating sequences of codes.
The construct
specification required by nomological nets is one way of
ensuring that codes,
sequences of codes, and recurring patterns relate to
theoretically meaningful
constructs. Thus nomological nets can assist researchers in
determining whether
“discovered” patterns have psychological validity and practical
utility.
Construct specification in
nets
can also assist with an additional “sticky wicket” in efficient
yet automated
detection of meaningful patterns of interactions. Essentially,
over what time
frame are patterns of interaction to be detected and at what
levels? That is,
if a pattern is detected in a series of successive turns does
that pattern then
become a “unit” that can act as input to a subsequent pattern
analysis effort?
How are such patterns related to constructs in the net? One
might envision a
series of intermediate level patterns being inferred from turn
by turn coding.
These intermediate levels (essentially patterned sequences of
turns) are the
units upon which further pattern detection analyses are
conducted. Determining
and optimizing appropriate time
scales over which patterns of code sequences are constituted
depends on
understanding the intentions and assumptions of specific
designed learning
environments, particularly what and when specific processes are
expected; why,
and how they support expected outcomes. Although patterns and
sequences can be
detected automatically it will take human interpretive lenses
and
socio-cognitive theories to specify the constructs these index
and their
meaningfulness in the context of learning. The issue of levels
is relevant not
only to pattern detection but to learning in general, as
reflected in the
fourth aspect of a more complex view of learning.
4.
The effects of learning
are evident at multiple levels ranging from behavioural to
neural.
Learning is “visible” at
different levels. De Smedt (this issue) is to be applauded for
emphasizing the
need for alignment between the level and topical focus of
research questions
and the methods selected to investigate the questions. He
pointed out that if
the research question is targeted at the macrolevel, behavioral
methods would
be most appropriate. Cognitive neuroscience methods become
appropriate for
research questions focused on microlevel processes. The two most
common
cognitive neuroscience methods are electroencephalography (EEG)
and functional
Magnetic Resonance Imaging (fMRI). EEG methods provide temporal
information
about when particular processes are taking place and fMRI
methods provide
spatial information about where in the brain processes are
taking place.
Cognitive theories provide needed links between behavioral and
neural levels.
Furthermore, echoing the
point
made above – that socio - cognitive theory needs to guide the
interpretation of
patterns in interactions, De Smedt (this issue) called for
detailed cognitive
theory of learning phenomena to provide needed links between
behavioral and
neural levels. He cited the cognitive theories and the
behavioral data on which
they are based as critical for interpretations of the
information that results from
the application of cognitive neuroscience methods. To
demonstrate his claims,
De Smedt (this issue) illustrated three ways in which cognitive
neuroscience
methods elucidate mathematical instruction. These are convincing
demonstrations
of the value added of obtaining data on the same phenomenon at
multiple levels
and coordinating findings across levels. Predictions can be
pursued across
levels by postulating what should be the case at one level based
on
manifestations at another level.
The value added of using
multiple
methods to examine learning at multiple levels is not restricted
to
mathematics. For example, in the area of language acquisition,
researchers have
used EEG methods to establish predictive relationships between
phonemic and
word-level development. Specifically, infants below six months
of age are
sensitive to phonetic contrasts in all languages; between six
and 10 months, a
perceptual narrowing process occurs that results in sensitivity
to only those
phonetic contrasts that matter in their native language. EEG
methods have
established that better neural discrimination of native language
phonetic
contrasts is associated with faster vocabulary development (Kuhl
&
Rivera-Gaxiola, 2008). Kuhl and colleagues have also used neural
activation patterns
to determine that the perceptual narrowing process occurs
several months later
for infants reared in two-language homes compared to those
reared in
monolingual homes. Finally, neural indicators of phoneme
learning demonstrate
that social interaction plays a critical role in language
acquisition (Kuhl,
2007). In each of these cases, evidence from the neural level
provides measures
of far greater precision than could be obtained behaviorally.
5.
Summary and Challenges
More complex views of
learning
require more complex methodologies for addressing key questions
about learning
and the conditions that support it, including explicit
instruction. The four
papers in this special issue illustrate methodologies that
assist with
capturing the iterative design-based research process, learning
processes and
outcomes that occur at different time scales and levels, and
make possible the
formulation and testing of hypotheses that relate different
levels to one
another. The papers present examples of ways in which these
methodologies are
augmenting the knowledge base for understanding learning as it
occurs across
individuals as well as within individuals. As such they make
valuable
contributions to the field.Moving forward, there are a number of
areas that
need attention in terms of further theoretical and
methodological development.
Briefly, more emphasis needs to be devoted to formative
assessment that
provides opportunities to better facilitate instructional
processes and
outcomes. This includes the design and testing of tools for
capturing learning
interactions that are classroom, teacher, and student friendly.
Such tools
would enable students and teachers to reflect on their learning
processes as
well as outcomes at much finer levels of detail than is
currently feasible.
Ideally, researchers would develop and test various
technology-based tools for
accomplishing these goals and would then engage in “user
testing” of tools that
travel outside research labs and into the hands of teachers and
learners. A
type of tool that would be helpful in this process is one that
enables
visualizations of the ebb and flow of learning processes across
people and
across time.
Finally, the Learning
Sciences
community has tended to design within specific disciplines and
fields; the
Learning and Instruction community has tended to test principles
and variables
thought of as general across all learning situations. Neither
perspective has
as yet come to grips with the tension between generalist and
discipline-specific views of learning nor the limitations of
each view. What is
needed are studies that 1) embrace a
disciplinary perspective but that also situate that discipline
in the context
of epistemological orientations and inquiry methods that have
been adopted and
developed within other disciplinary communities; and 2) examine
the “fit” of
principles, constructs, and explanatory mechanisms suggested by
cognitive,
developmental, and social psychological research to learning
phenomena observed
in designed learning environments. Studies of the first type
will advance our
understanding of the general and idiosyncratic aspects of
learning in different
disciplines. Studies of the second type will advance our
understanding of
explanatory mechanisms that have traction across a wide versus
narrow band of
learners and situations of learning. There are also aspects of
learning
processes and outcomes that need far more systematic and
sustained research
over shorter and longer time scales. Specifically, we need to
conduct
systematic research on relationships among persistence,
engagement, identity,
learning processes and outcomes, within and across formal and
informal contexts
of learning. Some of this research is currently being conducted;
more of it
needs to be conducted. The methodologies discussed in these
papers can be
synergistic with respect to tackling these challenges.
Keypoints
Acknowledgments
The writing of this paper
was
supported, in part, by the Institute of Education Sciences, U.S.
Department of
Education, through Grant R305F100007 to University of Illinois
at Chicago. The
opinions expressed are those of the authors and do not represent
views of the
Institute or the U.S. Department of Education.
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Figure 1. (see pdf file)
Representation of the discourse moves of three students engaged
in a science
inquiry task. (cf. Radinsky, J. L.,
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Negotiating What Is Seen
and What It Means. Paper presented at the annual meeting
of the American
Educational Research Association, Denver, CO.)