Scientific Reasoning and Argumentation:
Advancing an Interdisciplinary Research Agenda in Education
Frank Fischera, Ingo Kollara,
Stefan Uferb, Beate Sodiana, Heinrich
Hussmannc, Reinhard Pekruna, Birgit
Neuhausd, Birgit Dornere, Sabine
Pankofere, Martin Fischerf, Jan-Willem
Strijbosa, Moritz Heenea & Julia
Eberlea,d
a
Ludwig
Maximilians University of Munich, Department of Psychology,
Germany
b
Ludwig
Maximilians University of Munich, Department of Mathematics,
Germany
c
Ludwig
Maximilians University of Munich, Department of Informatics,
Germany
d
Ludwig
Maximilians University of Munich, Department of Biology,
Germany
e Katholische
Stiftungsfachhochschule München - University of Applied
Sciences, Germany
f Ludwig Maximilians
University of Munich, University Hospital, Institute for
Medical Education, Germany
a-f Munich Center of the
Learning Sciences, Germany
Article received 24
February 2014 / revised 1 April 2014 / accepted 19 May
2014 / available online 16 June 2014
Abstract
Scientific
reasoning and scientific argumentation are highly valued
outcomes of K-12 and higher education. In this article, we
first review main topics and key findings of three different
strands of research, namely research on the development of
scientific reasoning, research on scientific argumentation,
and research on approaches to support scientific reasoning and
argumentation. Building on these findings, we outline current
research deficits and address five aspects that exemplify
where and how research on scientific reasoning and
argumentation needs to be expanded. In particular, we suggest
to ground future research in a conceptual framework with three
epistemic modes (advancing theory building about natural and
social phenomena, artefact-centred scientific reasoning, and
science-based reasoning in practice) and eight epistemic
activities (problem identification, questioning, hypothesis
generation, construction and redesign of artefacts, evidence
generation, evidence evaluation, drawing conclusions as well
as communicating and scrutinizing scientific reasoning and its
results). We further propose addressing the domain
specificities and domain generalities of scientific reasoning
and argumentation as well as approaches for facilitation.
Finally, we argue for investigating the role of epistemic
emotions, the role of the social context, and the influence of
digital technologies on scientific reasoning and
argumentation.
Keywords: scientific reasoning; argumentation; epistemic
emotions; collaboration; technology
1.
Problem
To participate in the
knowledge society and to benefit from the unprecedented open
access to a vast volume of scientific knowledge requires a broad
set of skills and abilities that have lately been labelled as 21st
century skills (e.g., Trilling, & Fadel, 2009). These
include skills and abilities to use scientific concepts and
methods to understand how scientific knowledge is generated in
different scientific disciplines, to evaluate the validity of
science-related claims, to assess the relevance of new
scientific concepts, methods, and findings, and to generate new
knowledge using these concepts and methods. The acquisition of
these complex competencies is considered a main goal and outcome
of K-12 and higher education. However, contemporary knowledge
about what constitutes these competencies and how they can be
facilitated is scattered over different research disciplines.
In order to develop a
better understanding of these competencies, we propose to build
on three existing strands of research. First, research on the
development of scientific reasoning (e.g., Koslowski, 2012);
second, research looking at the processes and products of
scientific argumentation (e.g., Chinn & Clark, 2013) from
the fields of educational psychology, education, as well as
science education and other subject education disciplines.
Third, there is a broad range of approaches to support and
facilitate scientific reasoning and argumentation (SRA) in
educational contexts (e.g., Furtak, Seidel, Iverson, &
Briggs, 2012). In this article, we will first provide an
overview of the main topics and key findings of these three
strands of research. Building on these findings, we outline the
deficits of existing research and address five aspects that
exemplify where and how research on SRA needs to be expanded.
2.
Key Findings of Previous Research
2.1 Development
of Scientific Reasoning
Research on scientific
reasoning amongst laypeople has its roots in developmental
psychology. Inhelder and Piaget (1958) assumed that scientific
rationality was a model of the ideal human reasoning, that is, a
person who reflects on theories, builds hypothetical models of
reality, critically and exhaustively tests for all possible main
and interaction effects between variables, and objectively and
systematically evaluates evidence with respect to a claim. In a
series of studies they showed that the scientific reasoning of
preadolescent children was severely deficient, whereas
significant improvement took place in adolescence. These
findings led them to claim the stage of “formal operational
thought” as the highest stage of cognitive development. This
view has since been heavily criticised, as it neither adequately
captures adult reasoning nor its development (Kuhn &
Franklin, 2006).
Neither the lay adult nor
professional scientists conform to a model of domain-general,
ideal scientific rationality. Rather, adult reasoning abilities
are heavily dependent on domain-specific knowledge and context
(e.g., Kruglanski & Gigerenzer, 2011). This is found for
laypersons, but professional scientists are equally influenced
by their prior knowledge and theoretical biases (Dunbar, 1995).
Similarly, children’s scientific reasoning is context and task
dependent and does not differ fundamentally from adult
scientific reasoning (Koslowski, 1996, 2012; see Zimmerman,
2000, 2007).
The “layperson as
scientist” metaphor, which focuses on processes of intentional
knowledge seeking to test theories and hypotheses and to
evaluate evidence with respect to a hypothesis or theory (Kuhn
& Franklin, 2006), has proved to be a productive framework
for research into scientific reasoning. However, broad models of
scientific reasoning that incorporate early competencies are
only now emerging (Kuhn, & Franklin, 2006; Sodian &
Bullock, 2008). For example, Kuhn (1991) showed that
differentiation of theory and evidence poses a major problem for
many lay adults in complex, real-world argumentation. However,
even young elementary school children can differentiate
hypothetical beliefs from evidence and identify a conclusive
research design to test a hypothesis (Sodian, Zaitchik &
Carey, 1991). Third graders distinguish controlled from
confounded experiments (Bullock, & Ziegler, 1999). Even
pre-schoolers possess basic data evaluation competencies
(Koerber, Sodian, Thoermer, & Nett 2005; Koerber b&
Sodian, 2009). Thus, neither children nor adults appear to lack
a basic understanding of the relationship between hypothetical
beliefs and empirical evidence. Rather, in complex theory
evaluation tasks, both children and adults appear to lack an
understanding of mechanisms, as well as methodological knowledge
to provide and judge evidence-based arguments (e.g., Koslowski,
2012).
A meta-conceptual
understanding of the nature of scientific knowledge has been
identified as a major source of developmental progress.
Understanding progresses from an undifferentiated Level 1
(science as activities and effects) through an intermediate
Level 2 (science as providing explanations via testable claims)
to a Level 3 understanding (science as a cyclical and cumulative
process of theory, testing, and revision), with children rarely
displaying Level 2 and even adults rarely articulating a
coherent Level 3 understanding (e.g., Carey & Smith, 1993).
However, even the nature of elementary school students’ science
understanding can be improved through instructional support
(e.g., Sodian, Jonen, Thoermer, & Kircher, 2006). Moreover,
an advanced meta-conceptual understanding of science in
childhood has been found to predict strategy acquisition in
adolescence (Bullock, Sodian, & Koerber, 2009).
Recent attempts in
developmental research with elementary school students support a
model of scientific reasoning as a complex set of interrelated
abilities, consisting of four major components: “understanding
the nature of science”, “understanding theories”, “designing
experiments”, and “interpreting data” (e.g., Koerber, Sodian,
Kropf, Mayer, & Schwippert, 2011). Apart from general
cognitive abilities, student’s problem-solving skills and
spatial abilities have been shown to have a major impact on
these scientific reasoning competencies. Moreover, scientific
reasoning has been shown to be a separate construct from
measures of intelligence and reading skills in elementary school
students (Mayer, Sodian, Koerber, & Schwippert, 2014).
2.2 Scientific
Argumentation
While developmental
research is mainly interested in the developmental trajectories
of an individual’s scientific reasoning, educational and science
education research on scientific argumentation has focused on
the externalised processes and products of scientific reasoning
within social contexts (e.g., the science classroom; Osborne,
2010). The interest in scientific argumentation is sparked by
the view that argumentation relates to the learning of core
content and acquisition of general argumentation skills (Chinn
& Clark, 2013). Previous research strived for two main
goals: (a) identification of students’ deficits during their
engagement in scientific argumentation in social contexts, and
(b) design and development of effective scaffolding approaches
to improve students’ argumentation.
With respect to students’
deficits in scientific argumentation, some studies focused on
the structural quality of student-generated arguments, for
example on the use of evidence (e.g., McNeill, 2011), qualifiers
(Stegmann, Wecker, Weinberger, & Fischer, 2012) or warrants
(Kollar, Fischer, & Slotta, 2007). A recurring finding has
been that students tend to make claims without justifications.
In socio-scientific debates, they typically do not spontaneously
refer to scientific concepts and information (Sadler, 2004).
Other studies have shown that students often have problems
producing arguments of high content quality (e.g., Kelly, &
Takao, 2002). A third set of studies revealed that students
often exhibit a poor dialogic or social quality of argumentation
as reflected in the social exchange and co-construction of
arguments. For example, students have been found to refrain from
challenging others’ arguments (Weinberger, Stegmann, &
Fischer, 2010). This might be related to the recurring finding
that students have difficulties recognising contrasting
argumentative positions (Sadler, 2004) and are often not
successful in integrating different perspectives of different
learners within a group or community (Noroozi, Weinberger,
Biermans, Mulder, & Chizari, 2012).
2.3 Intervention
Studies
How students can
effectively be supported in their acquisition of SRA-related
skills has been subject to a large body of intervention-based
research, including long-term and short-term interventions,
technology-based and teacher-based scaffolding, laboratory as
well as field studies, and studies at the school and university
levels (e.g., Kollar et al., 2007; McNeill, Lizotte, Krajcik
& Marx, 2006). Overall, this research shows that SRA can be
substantially advanced by making it an explicit topic of
instruction (see Osborne, 2010). This applies to both increasing
students’ abilities to engage in activities of scientific
knowledge generation (or epistemic activities) and helping them
develop a more sophisticated understanding of the nature of
science. Current research on instructional approaches focuses on
immersing learners into scientific practices (see Cavagnetto,
2010) which typically involves student engagement in
research-related activities and debates. Three prototypical
instructional approaches are inquiry learning, problem-based
learning, and design-based learning. Inquiry learning engages
students in more or less authentic activities of hypothesis
formulation, generation of evidence, and drawing conclusions
(Chinn & Malhotra, 2002). Inquiry learning proved to be an
effective instructional approach to advance science learning,
especially when combined with teacher-led activities (e.g.,
Furtak et al., 2012). Similarly, in problem-based learning,
students are confronted with complex problems and expected to
find explanations and solutions that are based on scientific
concepts and methods (e.g., Dochy, Segers, van den Bossche &
Gijbels, 2003). Design-based learning (e.g., Kolodner, 2007)
engages students in inter-linked cycles of research and design
with the goal of arriving at an optimal design of a concrete
product, such as a miniature car that that can go from one end
of the classroom to the other.
In all of these approaches
that aim to immerse students into authentic SRA processes, it
has been found crucial to provide students with structural
support. This scaffolding may be directed at individual
learners, small groups and whole classrooms. For individual
learning, hints, prompts, sentence starters, and guiding
questions that help students focus their attention on the
critical aspects of SRA have been found to be effective (see
Quintana, Reiser, Davis, Krajcik, Fretz, Duncan, & Soloway,
2005). A hypothesis scratchpad, for example, helped students
formulate better hypotheses than students whose hypothesis
formation was unscaffolded (van Joolingen, & de Jong, 1993).
For small-group collaboration, several studies showed that the
quality of SRA can be raised substantially through collaboration
scripts (see Fischer, Kollar, Stegmann, & Wecker, 2013),
which assign roles to learners and sequence their epistemic
activities. For instance, a social-discursive peer review script
has been shown to enhance student argumentation. Detailed
process analyses revealed that social-discursive argumentation
during the peer review processes mediated the effects of
scaffolding by the script on the improvement of (individual)
argumentation skills (Stegmann et al., 2012). A related form of
structuring collaboration is peer assessment (e.g., Cho, Schunn,
& Wilson, 2006; Strijbos, & Sluijsmans, 2010), which is
also a crucial aspect of the contemporary scientific process.
Peer assessment can be used to help collaborators uncover
incongruence in their respective SRA processes when scrutinising
scientific claims and evidence. The incongruence can
subsequently foster refinement of target processes through
critical reflection (Nicol, Thomson, & Breslin, 2014).
Finally, studies demonstrated that teachers can be successfully
empowered to help students gain scientific argumentation skills
(e.g., Erduran, Simon, & Osborne, 2004). For instance,
research on classroom scripts has shown that epistemic
activities can be facilitated if teachers combine scaffolding at
different social levels in the classroom (plenary, group,
individual; e.g., Mäkitalo-Siegl, Kohnle, & Fischer, 2011).
Moving even beyond the boundaries of the classroom, knowledge building communities have been successfully
implemented in schools around the globe to engage students in
argumentative processes to jointly construct knowledge in the
classroom (Scardamalia & Bereiter, 2006).
3.
Deficits of Prior Research and
Directions for Advancing Studies on SRA
Research on the development of scientific reasoning, as
well as research on scientific argumentation, has substantially
progressed over the last two decades (see Nussbaum, 2011;
Zimmerman, 2007). However, there are still important research
gaps which leads us to argue for more systematic and
interdisciplinary research on SRA. We propose that future
research should (a) expand the range of epistemic modes and
epistemic activities, (b) investigate domain-specific aspects of
SRA more systematically, (c) examine the role of emotions in
SRA, (d) consider the social context of SRA in a more systematic
way, and (e) explore the influence of digital technologies on
SRA. Each of these suggestions is more closely elaborated upon
in the following.
3.1 Expanding
the Range of Epistemic Modes and Epistemic Activities
3.1.1 Epistemic Modes
People engage in SRA with different motivations. For
example, a researcher may strive to contribute to theory
building in a domain while practitioners try to find solutions
for problems in their professional practice by applying
scientific concepts or methods. We argue that these different
motivations have not yet been systematically reflected in
research on SRA in educational contexts. Stokes (1997) suggested
a widely accepted classification according to which approaches
to scientific reasoning vary in their primary goals along two
orthogonal dimensions: understanding and use. Pure basic
research is characterised by its primary goal of advancing
scientific understanding of natural and social phenomena,
regardless of its usefulness in practice. Stokes used Nils
Bohr’s scientific approach – with no emphasis on the use and
societal uptake of his theoretical advances – to characterise
this type of research. In contrast, pure applied research
emphasises the use of scientific knowledge without the aim of
advancing theory building and understanding. Stokes exemplified
this kind of research with the work of Thomas A. Edison, who
brought electricity to a whole country by using scientific
knowledge and methods, but without being concerned about
generalisation and theory building beyond this practical
challenge. A third class that Stokes (1997) identified is the
scientific approach that combines the goals of understanding and
use, which he termed “use-inspired basic research” and
exemplified with Louis Pasteur’s work. Pasteur started from
problems in practice (e.g., how to make food last longer),
conducted systematic research to solve them, but simultaneously
strived for a generalised theoretical explanation.
We suggest that Stokes’ classification of research
approaches can be used to inform the differentiation of three
distinct modes of SRA. In a first mode (1) SRA can be used to
advance theory building about natural and social phenomena. When
learners apply this mode, they aim to generate and test
hypotheses to develop and improve scientific theories and
explanations about social and natural phenomena. That way, this
epistemic mode will help support student learning of the
scientific knowledge of a domain, how it is created, and how
students themselves can contribute to knowledge creation by
engaging in scientific research.
A second SRA mode may be labelled (2) science-based
reasoning and argumentation in practice. In this mode, learners
aim at developing solutions for contextualised problems using
scientific concepts, theories, and methods. Based on information
about the problem and the state-of-research as they know it,
learners generate one or more solution approaches and evaluate
them in light of scientific knowledge and methods, but also
based on standards of the practice under consideration. That
way, learners take over the role of scientifically knowledgeable
practitioners rather than that of basic researchers. For
example, teacher education students may develop a concept to
help 4th graders improve with respect to their reading
abilities, based on both practice-based observations of the
possibly poor reading abilities of their students and on prior
scientific theories and empirical studies on how to effectively
support students with reading difficulties (e.g., Reciprocal
Teaching; Palincsar & Brown, 1984). Another example is the
application of mathematics to solve practical problems (e.g.,
predicting the development of sprint world records by describing
historical data with an appropriate mathematical function),
typically referred to as “mathematical modelling” (Galbraith,
Henn, & Niss, 2007). The difference between science-based
reasoning in practice and problem solving is that the result is
not only the solution of a problem, but also an argument based
on scientific theory.
The third SRA mode we would like to introduce is called
(3) artefact-centred SRA. This mode is realized when students
engage in circular processes which involve the concurrent
development of an artefact and a scientific theory or
explanation for why the artefact works or does not work (i.e.,
why a given problem can or cannot be solved by the use of the
artefact), through repeated cycles of prototype design, testing,
and analysis of test results. For example, Kolodner (2007)
reports on a science curriculum unit during which students are
supposed to build miniature cars from a given set of materials.
Based on concepts from physics (e.g., friction and force), the
students’ task is to design a car that would travel from one end
of the classroom to the other. That way, the students’ reasoning
and argumentation resembles that of researchers in engineering
and technology. This mode of scientific reasoning differs from
“science-based reasoning in practice” with respect to the thrust
towards generalisation and theory building. Nevertheless, in
educational contexts, both modes have the potential to address
student competence of understanding and engagement in scientific
knowledge creation activities, as well as their competence to
address practical problems through application of scientific
concepts and methods.
3.1.2 Epistemic activities
The three epistemic modes imply an extended notion of SRA
that also calls for considering a comprehensive set of
scientific activities. Students in educational contexts need to
learn how these activities work and how to engage in them. We
suggest distinguishing eight epistemic activities that all may
be fulfilled in SRA in all of the three epistemic modes. Yet,
both the weight that is attributed to each activity in each of
the three modes and the way these activities are performed
within each of the three modes may differ. In the following, we
describe each of these activities along with one example of how
the activity may be performed in one of the three epistemic
modes.
(1) Problem identification. Many scientific reasoning
processes are driven by concrete problems. According to the
three epistemic modes, such problems might be practical
real-world problems (see Kolodner, 2007), but also scientific
problems that cannot be solved with the available theoretical
concepts and methods. Becoming aware that available explanations
do not appropriately explain phenomena is a starting point for
both the advancement of science as an abstract set of knowledge,
and for the individual learner advancing his or her
understanding of the world. Thus, to engage in SRA, one first
needs to perceive a mismatch or shortcoming concerning the
available explanation of a particular problem. During this
epistemic activity, a problem representation is built from an
analysis of the situation. A medical student may for example be
confronted with a patient who reports a diverse set of illness
symptoms (exemplifying the epistemic mode science-based SRA in
practice). Based on medical knowledge, which in medical experts
typically is encapsulated in so-called “illness scripts”
(Charlin, Boshuizen, Custers, & Feltovich, 2007), the
student will try to identify which parts of the patient’s
descriptions are relevant for the diagnostic process and which
are not. That way, the actual biomedical problem is gradually
concretized and then determines further action.
(2) Questioning. Based on the representation developed
during problem identification, one or more initial questions are
identified for the subsequent reasoning process (see White &
Frederiksen, 1998). Later on, this question might be refined to
allow for a systematic search of evidence. To exemplify how a
math student may be confronted with questioning in the epistemic
mode of advancing theory building about natural and social
phenomena we refer to the following famous problem formulated by
Euler in 1741 (Seven Bridges of Königsberg; solution proved by
Hierholzer & Wiener, 1873): In a given arrangement of points
and lines between these points (e.g., a set of crossings and
streets in a city), how can we determine if an “Euler-Walk”
along adjacent lines is possible, which passes each line exactly
once (e.g., a sightseeing walk through the city)? The problem
here is a classification problem (how to describe objects with a
given property).
(3) Hypothesis generation. During hypothesis generation,
students derive possible answers to the question from plausible
models, available theoretical frameworks or empirical evidence
they are aware of (Klahr & Dunbar, 1988). If the student’s
prior knowledge does not allow for predictions, the question
might be refined or – alternatively – an exploratory approach of
evidence generation may be adopted to derive a hypothesis based
on patterns in this evidence. This process involves formulating
the hypothesis according to scientific standards. In biology, a
learner may for example aim at developing an answer to the
question how the memory of honey bees develops. Based on prior
research, the learner may hypothesize that glutamate plays a
role in this process, since glutamate has been shown to be
important for human memory development. To substantiate this
hypothesis, further search for corresponding literature may be
necessary, e.g. concerning the question whether glutamate has
also been found in other insects.
(4) Construction and redesign of artefacts. Scientific
reasoning often includes the construction of some kind of
artefact, be it the development of a prototype object by an
engineer or an axiomatic system describing a new mathematical
structure. Typically, this construction will be based on current
theoretical knowledge. Following its construction, the artefact
is submitted to a test in an authentic environment (see
Kolodner, 2007). For example, teacher students may have the task
to develop a computer-based collaborative learning environment
that would effectively scaffold the interaction of small groups
of learners in order to raise the individuals’ learning outcomes
(exemplifying the epistemic mode of artefact-centred SRA). For
that purpose, a prototype of the learning environment (e.g.,
based on the collaboration script approach; Fischer et al.,
2013) may be built that – based on theoretical reasoning and
prior empirical evidence – seems promising to achieve this goal.
(5) Evidence generation. Evidence generation includes
various approaches. One approach is to conduct
hypothetico-deductive experimental studies that refer to the
systematic, theory-driven variation of one or more variables by
the learner in consecutive trials, while repeatedly observing
the same outcome variables. Evidence generation may also follow
an inductive approach of observing, comparing and describing
phenomena to draw conclusions about structures and functions,
for example in evolutionary biology or sociology. Another
approach is observing the synchronous or sequential
co-occurrence of phenomena, which is frequently applied in the
natural sciences (e.g., when studying climate models), but also
in the social sciences (e.g., in longitudinal studies). Finally,
most natural and social sciences use deductive reasoning –
within more or less elaborate theories – to generate evidence in
favour or against a claim. In the mathematic example by Euler
described above, a first approach to gather (exploratory, in the
mathematical sense preliminary) evidence would be to study
single examples of point-line configurations and test if they
admit an Euler-Walk. Comparing configurations which admit such a
walk and some which do not, might lead to a first hypothesis
about the characteristic difference between the two (Hypothesis
generation). Studying more, and perhaps extreme, examples will
add further (still preliminary) evidence to support the
hypothesis, maybe leading to its revision or refinement.
Finally, starting from a set of basic assumptions on such line
configurations (described by the axioms of mathematical Graph
Theory), a deductive chain of arguments can be constructed that
shows that configurations admitting an Euler-Walk have the
hypothesized property, and vice versa. Constructing such a line
of deductive arguments, which derive that a conjecture follows
from the axioms of a mathematical theory, is actually the main
mode of evidence generation in mathematics. Nevertheless, also
other kinds of evidence play a major role in mathematical
reasoning, such as counter-examples that disprove a general
conjecture (e.g., Zazkis, & Chernoff, 2008).
(6) Evidence evaluation. The aim of evidence evaluation
is to assess the degree to which a certain piece of evidence
supports a claim or theory. What counts as evidence will differ
both with respect to the epistemic mode in which SRA is realised
and with respect to the domain under study. Observational
studies (Shafto, Kemp, Bonawitz, Coley, & Tenenbaum, 2008),
for example, might be considered the best available evidence in
one discipline (e.g., astronomy) but less valuable than
experimental studies in another (e.g., psychology, engineering;
Kolodner, 2007). Deductions from a theoretical framework
constitute the crucial acceptance criterion in mathematics,
whereas in psychology or in natural sciences they serve an
auxiliary role as predictions about the outcomes of an
experiment from theoretical assumptions. Even though an
“experimentum crucis” is not viable in most disciplines,
cumulated evidence from several experimental or observational
studies is necessary to sustain a claim. An example from medical
education in the epistemic mode of science-based SRA in practice
would be a medical student aiming to find the right diagnosis
for a patient’s health problem in a case-based simulation
environment. Evidence evaluation in this example may refer to
the accumulating evidence from the patient´s history, physical
examinations and additional lab and technical tests. Optimally,
this evidence is interpreted in light of candidate diagnoses
that have already been set up during hypothesis generation. Here
the development of encapsulated, experiential knowledge in the
form of illness scripts (Charlin et al., 2007) has been
identified as crucial in order to arrive at a sound evaluation
of the collected evidence.
(7) Drawing conclusions. Since different kinds of
evidence can be generated within the scientific reasoning
process, drawing conclusions is not restricted to reconsidering
an initial claim in light of experimental results. Different
pieces of evidence must often be integrated by weighing each
single piece according to the method by which it was generated
and by the rules and criteria of the discipline. In the case of
a teacher student developing a scaffolded computer-supported
learning environment, drawing conclusions means to critically
analyse data and observations from an experiment or a field
trial in which the environment was used and to derive
consequences for whether the environment (or specific features
of it) needs to be re-designed or may be used as originally
planned in further trials. To arrive at such a conclusion,
typically a multitude of data sources needs to be considered
(e.g., individual knowledge tests, verbal protocols, data on
students’ motivation).
(8) Communicating and scrutinising.
Individual scientific reasoning processes and their results are
typically shared with and scrutinised by others (Shavelson &
Towne, 2002). Persons involved in scientific reasoning are more
or less constantly involved in conversations and discussions in
work groups or peer groups. These interactions might influence
scientific reasoning from problem identification to
knowledge-based interventions in practice situations. Thus,
social-discursive and dialogic argumentation is an integral
component of many scientific reasoning processes and should be
included when analysing and facilitating SRA in educational
contexts (e.g., Clark, Sampson, Weinberger & Erkens, 2007;
Sampson & Clark, 2009). In the biology example on the memory
of honey bees, communicating and scrutinising may play a double
role. On the one hand, if groups of learners work on the honey
bee problem, communication within the team is necessary to
secure that the research process is carried out in a rigorous
way, including arriving at a sound explanation for the
phenomenon under investigation. On the other hand, the research
process and outcomes are typically shared with the broader
community, e.g. in the form of plenary presentations.
3.2 Domain-Specific Aspects
of SRA Need to be Investigated More Systematically
While research on SRA focused on commonalities across
domains, investigations on the differences of SRA between
disciplines have been rare (e.g., Herrenkohl & Cornelius,
2013). In addition, the set of domains under consideration has
so far been small and seemingly arbitrary. One crucial question
is what role domain-specific conceptual knowledge plays for
successful SRA (e.g., Chinnappan, Ekanayake & Brown, 2011;
Schunn & Anderson, 1999). Domain-specific conceptual
knowledge is, for example, necessary to build a mental
representation of the problem situation and to identify aspects
of the situation that offer scientifically accessible questions.
Moreover, the process of scientific reasoning is different
across domains, with respect to both nature and weight of the
epistemic activities to be displayed. For example, engineers
enact the epistemic activity of “problem identification” by
starting their design process with a clear problem for which the
initial stage, the solution stage, and the constraints are all
well-defined. Natural scientists and social scientists do not
necessarily have such well-defined initial and solution stages –
for them, thus, the epistemic activities “questioning” and
“hypothesis generation” play a major role. Regarding the
epistemic activity of “evidence evaluation”, scientific
disciplines vary considerably in what is regarded as acceptable
evidence to support a scientific claim. While many natural
sciences rely upon hypothetico-deductive methods, many social
sciences accept inductive comparisons as methods of evidence
evaluation. In (pure) mathematics the only acceptable evidence
is a chain of deductive arguments within a theory. All other
kinds of evidence are regarded as informal. Thus, transferring
criteria for evidence evaluation from one discipline to another
appears problematic. Moreover, it is unclear whether exposure to
one domain-specific approach of scientific reasoning influences
the nature of evidence evaluation skills in other domains (given
that K-12 education, as well as teacher education, immerses
students in various domains).
Although the nature of epistemic activities varies across
disciplines, approaches to foster student’s scientific reasoning
have typically focused on single domains and developed in
different directions. While research from developmental
psychology and science education has predominantly focused on
hypothesis and evidence generation and evaluation processes,
research from mathematics education focused on meta-cognitive
aspects to improve students’ self-regulated problem-solving (for
example when searching for mathematical proofs, Chinnappan &
Lawson, 1996).
Despite the fact that the existence of domain-dependent
differences concerning SRA can hardly be doubted, we contend
that the three epistemic modes and the eight epistemic
activities are of relevance to a broad range of disciplines. In
other words, there may also be skill aspects of SRA that are
similar across domains (such as skills for structuring a problem
situation, experimentation or deductive reasoning). However,
since disciplines might differ substantially in the relative
weights of the modes and activities and thus in the specific
knowledge, skills and attitudes that students are supposed to
develop when learning SRA, a more representative selection of
disciplines seems key for investigating their particularities in
future research.
Finally, existing approaches to facilitation have
typically proven effective for only one specific domain, in the
context of one epistemic mode, in referring to only some
specific epistemic activities, and in focusing on only some
specific learning prerequisites. The extent to which the
approaches to facilitation are domain-specific is an important
question, but the extent to which they can be generalised across
epistemic modes, domains, epistemic activities, and different
learners is an important question as well (see Klahr, Zimmerman
& Jirout, 2011). Future research should thus invest effort
in identifying domain-specific and domain-general aspects of SRA
and their facilitation.
3.3 The
Role of Emotions in SRA Requires Investigation
Cognition is intricately interwoven with emotions.
Emotions are defined as systems of interrelated component
processes, including subjective, physiological, and behavioral
components (e.g., uneasy and nervous feelings, physiological
activation, and anxious facial expression in anxiety; Shuman
& Scherer, 2014). Cognitive appraisals of situational
demands and one’s competencies are known to shape human emotion.
Emotions, in turn, are prime drivers of motivation to solve
problems and can profoundly impact the quality and outcomes of
cognitive processes (e.g., Moors, Ellsworth, Scherer &
Fijda, 2013; Pekrun, 2006). It seems likely that this is also
true for SRA. Without emotions such as surprise, curiosity
triggered by contradictory findings, joy about solving
scientific problems, or pride in one’s accomplishments,
scientists would likely not be motivated to engage in scientific
discovery, and students would lack motivation to learn science
(Pekrun, Hall, Goetz & Perry, in press). Furthermore, these
emotions are known to regulate attention, memory processes, and
different modes of cognitive problem solving, such as analytical
versus holistic ways to approach problems, which are critically
important for SRA (Fiedler & Beier, 2014). Systematic
research examining the links between emotions and scientific
reasoning, however, is largely lacking as yet (see Sinatra,
Broughton & Lombardi, 2014). We propose that five groups of
emotions that seem to be relevant for scientific reasoning
should be
investigated.
(1) Epistemic emotions. As noted, epistemic activities
such as generating hypotheses, are at the core of scientific
reasoning in a broad range of domains. Typically, these
activities are accompanied by emotions triggered by the
epistemic quality of problem-related information and mental
activity. A prototypical case is cognitive incongruity
triggering surprise, awe, curiosity, confusion, or joy when the
incongruity is resolved. As proposed by philosophers (Brun,
Doğuoğlu, & Kuenzle, 2008; Morton, 2010), these emotions can
be called epistemic emotions (Pekrun, & Stevens, 2011).
(2) Achievement emotions. Achievement emotions are
emotions that relate to activities or outcomes that are judged
according to competence-related standards of quality (Pekrun,
2006). In many learning situations, scientific reasoning
activities and the outcomes of these activities are judged for
their achievement quality. Depending on the perceived importance
of success and failure, scientific reasoning can induce strong
achievement emotions, such as hope and pride or anxiety, shame,
and hopelessness.
(3) Topic emotions. During scientific reasoning, emotions
can be triggered by the contents of the problem to be solved. An
example is the anxiety experienced when dealing with issues of
climate change or genetically modified food. In contrast to
epistemic emotions, topic emotions do not directly pertain to
the process of scientific reasoning, however, they can strongly
influence engagement in reasoning (Ainley, 2006).
(4) Social emotions. Scientific reasoning is often
situated in social contexts. By implication, scientific
reasoning can induce a multitude of social emotions related to
other people. These emotions include both social achievement
emotions, such as admiration, envy, contempt, or empathy related
to the success and failure of others, as well as non-achievement
emotions, such as love or hate in relationships with
collaborators in the reasoning process (Weiner, 2007).
(5) Incidental emotions and moods. When engaging in
scientific reasoning, a person can continue experiencing
emotions that relate to external events, such as current stress,
or problems in their family. These emotions do not relate to the
reasoning process itself, but have the potential, nonetheless,
to strongly influence the quality of reasoning and learning to
reason, such as a student’s worries about their parents’ divorce
being brought into the science classroom.
All five classes of emotion can play a role in all
epistemic activities. However, it seems likely that different
emotions are more typical for some of these activities than for
others. For example, epistemic emotions are likely to be
triggered by mental activities that can involve impasses and
cognitive incongruity, such as “problem identification” or
“evidence evaluation”, whereas social emotions are of primary
importance in collaborative reasoning processes and for the
communication of the results of scientific reasoning.
Furthermore, emotions of all five classes can profoundly
influence the scientific reasoning process and its outcomes. The
impact of these emotions on reasoning can be mediated by various
cognitive and motivational processes, e.g. intrinsic and
extrinsic motivation to engage, or deep versus shallow
information processing strategies (e.g., Clore & Huntsinger,
2009). As a consequence, positive activating emotions in
reasoning may typically support high-quality reasoning, whereas
some negative emotions may be detrimental. However, for many
emotions and task conditions, the effects on reasoning
performance are likely to be more complex. Thus we argue that
studying the role of emotions in and during SRA is an important
task for future research.
3.4 The
Social Context of SRA Should be Considered More Systematically
Scientific reasoning and argumentation are typically
situated in a social context (Dunbar, 1995). Some epistemic
activities are collaborative in nature, such as discussing the
results of scientific reasoning with peers or communicating them
to the broader public. Other epistemic activities are not
collaborative in nature but may benefit from collaboration (Chi,
2009; Duschl, 2008). We propose two strands for future research
that bear the potential to improve our understanding of the
social aspects of SRA.
(1) Collaborative knowledge construction. Extensive
research has been carried out on the cognitive and social
mechanisms of knowledge construction in groups and collectives.
Research on knowledge construction in pairs and small groups has
often been conducted in a joint problem-solving paradigm. This
line of research focuses on how pairs and groups, in contrast to
individuals, work on complex science-related problems (e.g.,
Okada & Simon, 1997) and on how groups develop joint
strategies and norms for SRA beyond just learning the domain
content associated with the task (e.g., Roschelle & Teasley,
1997). Research on dialogic education (Wegerif, 2007) and
argumentative classroom discourse (Osborne, 2010) focuses on the
structure and content of discussions in groups and collectives,
and on the conditions for evolving (scientific) quality of the
argumentation in these discussions. In contrast to the
perspectives on joint problem solving and dialogic argumentation
that analyse the micro-mechanisms of knowledge construction,
research on communities of practice emphasises processes of
knowledge creation, participation and identity in collectives of
people sharing goals or interests (Lave & Wenger, 1991). In
knowledge community approaches, domain knowledge acquisition by
individuals is rather seen as a by-product of qualitative
changes in the participation pattern, from legitimate peripheral
to more “core” participation. Research is needed on which forms
of participation in epistemic activities of certain scientific
communities effectively advance students’ SRA skills.
(2) Distributed, shared and collective cognition.
Approaches to distributed and shared cognition share the
assumption that reasoning in real world tasks cannot be
understood by just focussing on isolated individuals. In real
world tasks, individuals collaborate with others on solving
problems and making decisions, but they also use tools that
allow them to act much more intelligently than they would be
able to without. The distributed cognition perspective suggests
a systemic perspective for the analysis of complex social and
socio-technical tasks (e.g. Salomon & Perkins, 1998).
Research on transactive memory systems (Wegner, 1987) addresses
the cognitive interdependence that develops when group members
collaborate for some time and specialise in specific areas of
which the other members are aware. A transactive memory system
is thus characterised by the collaborative division of labour
for learning, remembering, and communication of knowledge (e.g.,
Hollingshead, Gupta, Yoon, & Brandon, 2011), which seems
crucial for most epistemic activities. The shared mental models
perspective (e.g. Mohammed & Dumville, 2001; Wu &
Keysar, 2007) addresses the question which kind of knowledge
(e.g., knowledge on task vs. knowledge on team) is needed and
the extent to which group members need overlapping (shared)
information as opposed to unique (or unshared) information to
perform well as a team. Research on cognitive convergence
(Teasley et al., 2008) or knowledge convergence (Fischer &
Mandl, 2005) focuses on the similarity and dissimilarity of
cognitive representations in collaborative situations, as well
as their changes through collaboration.
In the context of SRA it is an interesting open question
to which extent divergent vs. convergent cognitive
representations of different individuals in a group are
supportive for different epistemic activities. It seems
plausible to hypothesise that divergent knowledge in a group is
specifically supportive in epistemic activities such as
“evaluating evidence” and “scrutinising arguments”. Furthermore,
a recurring result from prior research is that the knowledge
learners acquire through collaboration is surprisingly
dissimilar (Miyake, 1986). This might be especially relevant for
educational settings where students engage in collaborative
learning to develop SRA skills. Research on expert-layperson
communication (Bromme, Jucks &
Runde, 2005) has shown that large differences in domain
expertise may have detrimental effects on communication and
understanding. Measures to support expert-layperson
communication have shown positive effects (e.g., Nückles &
Stürz, 2006). In the context of SRA these knowledge differences
exist, e.g., between scholars acting as teachers and students in
their early years but also in the context of communicating
scientific outcomes to wider audiences. An open question is how
different disciplines try to overcome detrimental effects and
make optimal use of large knowledge differences between scholars
and students.
3.5 The
Influence of Digital Technologies on SRA Needs Further
Research
Studies show that digital technologies affect reasoning
and learning contingent to the way that they are used. For
instance, a study by Sparrow, Liu and Wegner (2011) revealed
that digital technologies increasingly become “external
memories” integral to people’s reasoning. Their findings show
that the availability of externally stored information changes
cognitive processing dramatically, depending on the person’s
assumptions on later accessibility. It is plausible to assume
that the availability of digital technology affects SRA in a
similar way. Moreover, this should be true for all three
epistemic modes, but in different ways. When advancing theory
about natural and social phenomena, technology is typically used
for data collection and visualisation (e.g., computer
simulations; Gijlers & de Jong, 2009) as well as for
analysis, including not only statistical analysis but also
analysis based on language and logic (e.g., Rosé, Wang,
Arguello, Stegmann, Weinberger & Fischer, 2008). When
applying science-based reasoning in practice, technology is
often used to provide access to the knowledge base and theories
in the respective domain (e.g., Sparrow, et al., 2011). In the
epistemic mode of artefact-centred scientific reasoning,
technology often acts as the core enabler for prototypes or
simulating features of a design artefact (e.g., Wiethoff,
Schneider, Rohs, Butz & Greenberg, 2012). Across the three
modes, research has generated evidence that communication and
collaboration can be substantially enhanced by digital
technologies (see Stegmann et al., 2012). Furthermore, awareness
tools, i.e. tools capable of capturing and mirroring the quality
of group processes via external representations, have shown
strong potential to support scientific argumentation (Janssen
& Bodemer, 2013; Streng, Stegmann, Boring, Böhm, Fischer
& Hussmann, 2010).
We propose that future research should investigate how
technologies shape SRA. Firstly, research should more
systematically address how available and easily accessible
technologies influence scientific reasoning in the different
epistemic modes and activities. A co-evolutionary perspective on
the mutual influence of technology development and scientific
reasoning seems promising, for example, how access to scientific
information through the internet affects the SRA of
practitioners. Secondly, research should investigate the effects
of technological tools specifically designed to facilitate
certain epistemic activities in SRA. Prior research on computer
simulations and computer-supported collaboration will be
informative for the formulation of design principles for the
development of technology-based scaffolds.
4.
Conclusions
SRA is considered one of the core competences in
knowledge societies (Trilling & Fadel, 2009). Knowledge of
the structure and generality of these competences, their
emotional, social and technological conditions, and how they can
be facilitated appears key for a promising re-design of
curricula and interventions in schools, higher education and
vocational practice to foster the development of SRA. As a
starting point for the necessary interdisciplinary research we
suggest the following broad definition of SRA: Scientific reasoning and
argumentation include the knowledge and skills involved in
different epistemic activities (problem identification,
questioning, hypothesis generation, construction of artefacts,
evidence generation, evidence evaluation, drawing conclusions as
well as communicating and scrutinising scientific reasoning and
its results) in the context of three different epistemic modes
(advancing theory building about natural and social phenomena,
science-based reasoning in practice, and artefact-centred
scientific reasoning,). Scientific reasoning and argumentation
are assumed to consist of domain-specific as well as
domain-general components, and depend on emotional, social,
instructional/facilitative, and technological conditions.
We proposed a research agenda on the analysis and
facilitation of SRA in educational contexts, which significantly
broadens our perspective beyond basic experimental research.
Based on Stokes’ (1997) model of scientific knowledge
production, we suggested three epistemic modes of SRA: (1)
advancing theory building about natural and social phenomena,
(2) science-based reasoning in practice, and (2)
artefact-centred scientific reasoning. In a broad range of
domains, all three epistemic modes play a role. Students thus
need to learn to understand how scientific knowledge is
developed in their domains of study, and how it can be applied
to address practical problems. To an extent differing vastly
between domains and study programmes, students are also expected
to learn to participate in processes of scientific research
(Trilling & Fadel, 2009).
We further identified eight epistemic activities, of
which some have only received marginal or narrowly focused
consideration in research on SRA, mainly in the experimental
paradigm: (1) Problem identification, (2) Questioning, (3)
Hypothesis generation, (4) Construction and redesign of
artefacts, (5) Evidence generation, (6) Evidence evaluation, (7)
Drawing conclusions and (8) Communicating and scrutinising. We
do not claim that this process typology is exhaustive, and do
not intend to conceal that others have developed alternative
typologies (e.g., van Joolingen, de Jong, Lazonder, Savelsbergh
& Manlove, 2005; White & Frederiksen, 1998). Instead it
is proposed as a starting point for an interdisciplinary
research agenda, to be modified in further theoretical
discussion and based on findings of empirical studies. Based on
this framework, we suggest five further areas in research on SRA
that require more systematic investigation.
First, research should investigate the differences
between disciplines regarding how epistemic modes and activities
are employed and to what extent knowledge generated within them
is considered as evidence for or against theories. We suggest
that it is crucial to advance our understanding of SRA by
determining which aspects are domain-general and which aspects
are specific for a single domain or group of domains (see Schunn
& Anderson, 1999).
Second, commonalities and differences between disciplines
are also likely to exist with respect to measures of
intervention and facilitation. On the one hand, some of the
interventions developed for a specific domain and context might
prove generalizable to some extent to other contexts and
domains. On the other hand, domain-independent instructional
approaches might well be differentially effective in different
domains (see Klahr et al., 2011). In addition, we suggest
building a coherent conceptual framework for integrating the
diverse research findings from intervention research across
domains. Chi’s (2009) ICAP model might be a promising starting
point in this respect to integrate the available evidence and
guide future research on SRA interventions. ICAP classifies
learning activities based on their underlying cognitive
processes into interactive, constructive, active and passive.
The model predicts the best learning outcomes for interactive
learning activities, followed by constructive, active and
passive activities.
Third, research on SRA displays a strong cognitive bias.
However, it seems likely that most scientific reasoning
processes are triggered, modulated, or followed by emotions (see
Shuman, & Scherer, 2014). Thus far there is no systematic
research on emotions in the context of SRA, which is striking
because, for example, curiosity is widely regarded as a major
driving force for any scientific endeavour (Pekrun &
Stevens, 2011).
Fourth, scientific reasoning is increasingly recognised
as a social epistemic practice rather than a purely individual
activity (Dunbar, 1995). However, prior research on SRA has
examined the social context in which SRA appears in a rather
unsystematic way. Therefore, we suggest considering constructs
of research fields that are advanced in this perspective, such
as peer assessment (Cho et al., 2006; Strijbos & Sluijsmans,
2010) or research on collaboration scripts (Fischer et al.,
2013), as starting points for addressing the social aspects of
scientific reasoning.
Fifth, recent years have seen an expansion of digital
technology in nearly every sector of society, including research
and related fields of practice. We argue that the effects of
digital technologies on SRA practices need to be examined more
systematically. Important questions include how digital
technologies are used to support scientific reasoning and how
technologies can be designed to support students in SRA (see for
example Gijlers & de Jong, 2009).
Given the amount of research in the fields of scientific
reasoning and scientific argumentation described at the outset
of this article, the field might benefit from an integrative
view that combines the so far largely separated strands of
research. With concerted and interdisciplinary research efforts,
we strongly believe that we may achieve a better understanding
of what SRA skills are, how they develop and how their
development can be supported effectively. The outcomes of this
research may subsequently inform educational practice to help
educate citizens who are able to participate in science-related
societal debates and make more systematic use of scientific
knowledge and skills.
Keypoints
Acknowledgements
This work was funded by the Elite Network of Bavaria.
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