Methodological
Advances in
Research on
Learning and Instruction and in the Learning Sciences
Frank Fischera,
Sanna
Järveläb
a
University of Munich, Germany
b University
of
Oulu, Finland
Recent years have seen a
dynamic
growth of research communities addressing conditions, processes
and outcomes of
learning in formal and informal environments. Two of them have
markedly
advanced the field: The community on research on learning and
instruction that
has been organized in the European Association for Research on
Learning and
Instruction (EARLI), and the learning sciences community,
including the
computer-supported collaborative learning community, organised
in the
International Society of the Learning Sciences (ISLS). In this
special issue we
bring together excellent young researchers from these two
communities who are
currently contributing to advancing the methodology. We are
convinced that the
methodological developments in these two communities have a lot
of
commonalities as the core phenomena under investigation and the
core questions
are related to conditions, processes and outcomes of learning.
Common for both
of these communities is that they have strong roots in cognitive
science.
However, we also assume that there are substantial differences
in these methodological
developments, as the foci of the two communities differ in
important respects.
Most importantly, the learning sciences have strong theoretical
roots in
situative cognition and socio-cultural approaches focusing on
learning
activities in authentic contexts. The main assumption underlying
this focus is
that knowledge is represented in activity structures rather than
solely in the
head (Greeno, 2006). Therefore, removing the activities of their
social and
physical contexts into which they belong will change their
nature and, hence,
research would lead to invalid results, because only a part of
the knowledge
that is relevant for effectively participating in a practice can
be
investigated. Given these assumptions, it comes as no surprise
that learning
sciences research focuses on learning in authentic activities in
contexts
rather than settings stripped off the context for reasons of
control in the
experimental studies. Besides experiments and mixed-method
approaches a core
methodology that originated in the clear need for alternatives
to
deductive-experimental methods for early phases of such field
research is
Design-Based Research with a cyclic process and the goal to
improve a practice
and to develop a modest and local theory. DBR has its origins in
seminal papers
by Ann Brown (1992) and by Allan Collins (1992) as well as in
influences coming
from computer science (see Hoadley & Van Haneghan, 2011). As
knowledge is
seen to be tied to activities in practices rather than to a
single individual,
units of analysis beyond the individual (e.g., network, or
activity) are rather
the rule than the exception in learning sciences research.
Explorations of
different units of analysis are happening in both communities,
of course, but they
are more pronounced in the learning sciences community. Due to
the theoretical
roots in socio-cultural thinking and situative cognition the
relation of the
social and material environment to individual cognition is at
the core of
theorizing in the learning sciences. This is perhaps most
obvious in research
on computer-supported collaborative learning (see Dillenbourg,
Järvelä &
Fischer, 2009).
As the activities or
practices
are seen as the core medium of knowing, and the practices differ
a lot between
communities, domains and disciplines, research in the learning
sciences has an
important focus on disciplinary practices (e.g. Herrenkohl &
Cornelius,
2013). As the use of tools is a key feature of any community,
tool
appropriation and use are important foci in learning sciences
research. In the
learning sciences, the concept of tool is often very broadly
defined ranging
from tools like scientific concepts to digital technologies.
Research in the learning
and
instruction community is characterized by a strong connection of
basic research
to applied field studies. The field has deeper roots into
experimental
psychology and general psychology of learning and motivation.
Traditionally,
research on learning and instruction has focused on basic
processes of
cognition and learning and then applied these principles to
teaching and
learning practices. For example, understanding metacognitive
processes in human
learning (Flavell, 1979) has led many research groups to making
effective
interventions to the classroom contexts (Azevedo & Hadwin,
2005). Also
research on self-regulated learning has tried to integrate
empirical evidence
on basic processes of cognition, motivation and emotion into
broader
applications and interventions in the classrooms, where
teacher’s role,
students’ activities and features of the learning environment
have been
synchronized to serve learning (e.g., Dignath, Buettner &
Langfeld, 2008).
In recent years, basic
research
on learning and instruction has been helpful for designing
powerful learning
environments, where knowledge about student’s cognitive,
motivational and
emotional processes and their individual differences has been
applied to
instructional design. For example, knowledge on scientific
reasoning and on
worked-out examples has been applied in developing guidance for
inquiry
learning (Mulder, Lazonder & De Jong, 2014) and
collaborative learning (Kollar,
Ufer, Reichersdorfer, Vogel, Fischer & Reiss, 2014).
In the learning and
instruction
community one of the current strong emphases is on
methodological orientations
linking learning research to natural science brain research. The
educational
neuroscience movement seems to be more pronounced in research on
learning and
instruction than in the learning sciences. This is consistent
with the deeper
roots of learning and instruction research in general and
experimental
psychology, which has developed a strong neuroscience
orientation over the last
years.
In addition, methodologies
are
being developed addressing the temporal characteristics of
learning. In both
communities, quantitative approaches to the analysis of temporal
aspects of the
learning process have been developed over the last years. It is
argued that the
explanatory power and the validity of the analyses can be
improved dramatically
by including the time information that has typically been
neglected in many
studies on individual and collaborative learning. In research on
learning and
instruction, this new focus has originated as a consequence of a
conceptual
shift, as Molenaar (this volume, p. XX)
puts it: “Constructs
formerly viewed as personal traits, such as self-regulated
learning and
motivation, are now conceptualized as a series of events that
unfold over
time”. There are several arguments in support for this point
also in recent
publications in the learning sciences (e.g., Reimann, 2009).
There are four main potentials for innovation resulting from
these
developments for learning research, no matter if situated in
research on
learning and instruction or in learning sciences research.
Potential #1: Increased gain in
scientific understanding
through more “messy studies” when investigating “real”
learning in new fields.
It seems inadequate to presume a purely deductive experimental
approach in
fields where the set of potentially influential variables is
unknown. Learning
research is not an exception here, the same applies to other
fields like, e.g.
physics, where pioneering research at the edges of current
scientific knowledge
is more “messy” as well (Wieman, 2014). DBR approaches, although
still in their
infancies, might well develop into a standard methodology for
pioneering
research on “real learning“ in authentic settings, also in
research on learning
and instruction. In this special issue, Svihla (this volume)
reports on recent
developments in DBR that address the issues of scalability and
generalizability: Design-based implementation research (DBIR).
This might be a
promising alternative approach to randomized trial approaches to
implementation
research in fields where the set of influential and
to-be-controlled variables
in real formal and informal learning environments is far from
clear. Because of
its design focus, DBR and DBIR might contribute to advancing
learning research
beyond generating new scientific knowledge: They might have the
potential to
build bridges into practice and increase the credibility and
trustworthiness of
learning research. An alternative approach is suggested by
Stegmann (this
volume), who addresses the issue of control in studies of
complex,
collaborative learning environments. He argues for a more
systematic use of
nomological networks on the conceptual level in connection with
as-controlled-as-possible empirical studies that include
measures of learning processes
as their methodological core.
Potential
#2: More comprehensive understanding of learning phenomena
through the use of
methodologies that can handle multiple units of analysis and
include process
analyses. Units
like the activity, the group or the collective could become
standard for
questions that transcend the individual’s learning. It will be a
challenge how
to conceptually deal with this paradigm shift: talking about
“learning“ also
with respect to super-individual units. For example, should team
learning be considered
as a whole, or should the term “learning” be reserved for the
individual and
different concepts should be used to describe what is happening
in activities
or collectives? An even more far reaching question is to what
extent phenomena
on super-individual levels should be traced back (or be reduced
as some would
prefer to say) to the individual contribution, i.e. social
phenomena are
treated as a result of interacting individuals, and the
phenomena can be fully
explained by the individual contributions and reactions.
Increasingly there is
research arguing that some social phenomena in contexts of
learning cannot be
reasonably reduced to the individuals involved (Cress, Held
& Kimmerle,
2013; Eberle, Stegmann & Fischer, 2014; Stahl, 2006). In
this special
issue, Stegmann’s (this volume) work is additionally addressing
this aspect. He
describes measures of individual cognition and argumentative
discourse in
computer-supported small groups and exemplifies approaches to a
synchronized
analysis of individual cognition and group discourse to address
the mutual
impact. We argue that systematically employing other units of
analysis in
learning research than the individual would not only advance
research on
learning in context, but also help to build bridges into other
social sciences
that are sometimes hesitating because of the exclusivity of the
individual-centric perspective of some learning researchers.
Potential #3: Overcoming overreliance on
self-reports:
From personal constructs to series of interactions unfolding
over time.
Many learning researchers are currently working on developing
alternative
conceptualisations of well-established psychological constructs
such as
self-regulation or motivation. There are shortcomings of relying
solely on
self-reports in questionnaires (e.g. Zimmerman, 2008) to measure
personal
constructs, such as low predictive value for behaviour in real
problem-solving
situations. Learning researchers have therefore begun to develop
methodological
approaches that use behaviour or interaction in problem-solving
situations as
indicators for these constructs. An example from research in the
learning
sciences is Dan Hickeys work on disciplinary engagement in a
discussion
(Filsecker & Hickey, 2014) as a complementary measure of
motivation. In
this special issue, Inge Molenaar’s work is representing this
broader issue.
She focuses on the temporal characteristics of learning
processes that are
typically missed when only self-report measures are used or
observational data
is aggregated into frequencies over the whole learning process
under
consideration. Also recent advances in the use of
computer-generated trace data
for understanding patterns and processes of students’ learning
(Malmberg,
Järvenoja & Järvelä, 2013) have advanced the instructional
design field for
developing scaffolding and prompts for computer supported
learning (Järvelä
& Hadwin, 2013).
Potential # 4: Building bridges between
research on
learning and cognitive neuroscience. There have been
discussions if the gap
between education and neuroscience might require a bridge too
far. However,
recent advances in cognitive neuroscience are encouraging.
Research on learning
and instruction and in the learning sciences are increasingly
interested in the
biological basis of the learning phenomena under investigation
and some of
these ideas have already been applied e.g. to mathematics
learning (Hannula,
Lepola & Lehtinen, 2010). In the learning sciences and the
learning and
instruction community there is increasing awareness of the
possibilities to
analyse processes that are not readily accessible for
behavioural research. One
can hope that in the future, researchers on learning and
instruction and in the
learning sciences will be able to successfully point out
interesting learning
phenomena to neuroscientists (Varma, McCandliss & Schwartz,
2008). These
often complex and dynamic phenomena are typically highly
challenging for
contemporary neuroscientists. At the same time one can hope that
researchers in
learning and instruction as well as in the learning sciences
would become more
receptive for stimulations coming from unexplained phenomena in
neuroimaging
studies on cognition and learning. De Smedt (this volume)
addresses these
questions and elaborates on some convincing examples from
mathematics learning
that give evidence for a productive interaction between research
on learning
and instruction and cognitive neuroscience. He argues that the
successful
interaction crucially depends on finding the right level of
resolution or
granularity when involving neuroscience methods. We argue that
it is now a good
point in time to start exploring this interaction from both
research on
learning and instruction and in the learning sciences more
systematically. This
would enhance the interface of learning research to the natural
sciences. At
this interface there is a considerable potential for innovation.
Conclusion
Research on learning and
instruction and research in the learning sciences have seen
considerable
methodological advancements in recent years. Although a certain
specialisation
can be seen due to differences in some of the basic assumptions
we see good
reasons for transferring these innovations between the research
communities. We
see four potentials for innovation for learning research
resulting from these
methodological developments: (1) Increased gain in scientific
understanding
through more “messy studies” when investigating “real” learning
in new fields,
(2) more comprehensive understanding of learning phenomena
through the use of
methodologies that can handle multiple units of analysis and
entail processes
analyses, (3) overcoming overreliance on self-reports: From
personal constructs
of learning and motivation to series of interactions unfolding
over time, and
(4) building bridges between research on learning and cognitive
neuroscience.
The contributions to this
special
issue are each addressing one of these potentials.
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