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Title
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Knowledge Transfer
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Author
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Nokes‐Malach, Timothy J.
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Richey, J. Elizabeth
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Research Area
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Cognition and Emotions
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Topic
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Information Processing
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Abstract
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Controversy regarding the nature and frequency of knowledge transfer has received significant attention for more than a century, and this debate has sparked advances in our theoretical understanding of transfer as well as educational practices designed to promote it. We review the classical cognitive approach to studying transfer and highlight several important critiques of that approach regarding issues of context, assessment, and individual differences. These critiques have pushed research to improve understanding of the learning processes that facilitate transfer, the application processes that enact it, and the measurement of it. Research investigating the relationship between achievement goals and transfer serves as an example of the ways issues of context and individual differences are being integrated into the study of transfer. Future work on transfer should continue to refine and clarify how we define, assess, and promote it.
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Identifier
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etrds0197
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extracted text
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Knowledge Transfer
TIMOTHY J. NOKES-MALACH and J. ELIZABETH RICHEY
Abstract
Controversy regarding the nature and frequency of knowledge transfer has received
significant attention for more than a century, and this debate has sparked advances in
our theoretical understanding of transfer as well as educational practices designed to
promote it. We review the classical cognitive approach to studying transfer and highlight several important critiques of that approach regarding issues of context, assessment, and individual differences. These critiques have pushed research to improve
understanding of the learning processes that facilitate transfer, the application processes that enact it, and the measurement of it. Research investigating the relationship
between achievement goals and transfer serves as an example of the ways issues of
context and individual differences are being integrated into the study of transfer.
Future work on transfer should continue to refine and clarify how we define, assess,
and promote it.
INTRODUCTION
One of the most important functions of the human mind is the ability to use
prior knowledge and experience to solve novel problems. The learning and
cognitive sciences have called this the ability to transfer. Transfer appears evident in our day-to-day lives, from figuring out how to send an e-mail on a
new mobile device to finding one’s way in a foreign city to solving a problem
on an examination without having seen that problem before in class or on a
homework assignment. For each situation, transfer consists of some learning
or training experience (sending e-mail, interpreting maps, or solving homework problems) followed by a novel task in which some target knowledge or
skill acquired from learning is applied to that task. Understanding this ability
is critical for both psychology and education.
The importance of transfer for psychology is well illustrated with the following quote from Singley and Anderson’s seminal book, The Transfer of a
Cognitive Skill:
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
“To understand transfer, one must have detailed theories of both learning and
performance … In short, the study of transfer is a stringent but necessary test
for all comprehensive theories of cognition.”
(Singley & Anderson, 1989)
Theories of transfer bring together research on learning, memory, and problem solving to predict how and when we use prior knowledge to solve new
problems. Understanding transfer is also of critical importance for education.
This point is well illustrated by a quote from learning scientist Joanne Lobato:
“A central and enduring goal of education is to provide learning experiences
that are useful beyond the specific conditions of initial learning.”
(Lobato, 2006)
Transfer is not only a central goal of education, it is also an implicit guiding assumption for the design and construction of curricula, lectures, class
activities, and tests. Educators frequently assume (sometimes to our great
disappointment) that students will transfer what they know from one activity, lecture, class, or grade to the next. Given the importance of transfer to
these disciplines, it should not be surprising that there have been over 100
years of interest, research, and controversy on the topic.
CONTROVERSY
There were differing views at the beginning of the twentieth century
concerning whether or not transfer was likely to occur, and evidence
supporting both sides of the debate has continued to accumulate well into
the twenty-first century. In one view, transfer rarely occurs (Detterman,
1993; Thorndike & Woodworth, 1901) and in the other, transfer is pervasive
(Halpern, 1998; Judd, 1908). We believe these different views arise from the
complexity of the transfer phenomenon and mixed laboratory results on
the topic. Contrary to what the persistence of this debate might suggest,
we argue that much progress has been made in the psychological understanding of transfer, and this progress has been driven in part by mixed and
sometimes unexpected empirical results, as well as by differing theoretical
perspectives.
In this paper we identify some key reasons for the varying views of transfer,
summarizing the classic cognitive approach and critiques of that approach.
We then briefly review what we see as some major advances in understanding transfer and recent emerging trends. We conclude by highlighting what
this work suggests for resolving past controversies and charting new paths
forward.
Knowledge Transfer
3
CLASSICAL VIEW
Much cognitive psychology work on transfer in the 1970s, 1980s, and 1990s
can be categorized under this approach. The classical view focused on
understanding how different learning tasks affected the encoding of the
target knowledge, the nature of the resulting knowledge representation,
and factors influencing the subsequent application of that knowledge. A
basic laboratory paradigm used in this research consisted of a learning task
or problem(s) followed by a test problem(s). As researchers explored this
paradigm, they found transfer for some learning and test scenarios, but not
for others (for a review see Day & Goldstone, 2012).
A seminal study that illustrated a surprising case of a transfer failure is
Gick and Holyoak’s (1980) study of analogical problem solving. Participants in this study first read a story about a dictator’s fortress that was
overtaken by a military general who first separated and then converged
his troops simultaneously from multiple directions. The researchers then
tested whether participants would apply this convergence principle to
Duncker’s radiation problem, a case in which a patient’s tumor needed to
be destroyed by rays without harming the healthy tissue surrounding the
tumor. The research showed that contrary to expectations, college students
often failed to apply the just-learned convergence principle. This basic result
has been replicated many times (Anolli, Antonietti, Crisafulli, & Cantoia,
2001; Catrambone & Holyoak, 1989; Spencer & Weisberg, 1986) and has been
shown to occur for other tasks such as solving physics and math problems
when the cover story or domain at test was changed but the same concept
applied (Bassok & Holyoak, 1989; Bernardo, 2001; Novick, 1988). These
transfer failures have pushed research forward in the classical approach,
with an aim to determine why students failed in these situations; this led to
many advances and refinements in theories of learning and problem solving
(some of which we discuss here). At the same time, the failure to observe
transfer in the laboratory seemed at odds with the examples of real-world
transfer experiences with which we began the paper, and the approach
received critique from a number of different sources in the field (Beach, 1999;
Guberman & Greenfield, 1991; Lobato, 2006, 2012; Lobato & Siebert, 2002;
Pea, 1987). Many foundational critiques were captured by the influential
work of Jean Lave and colleagues working from a perspective of cognitive
anthropology (Lave, 1988). In the next section we describe three common
critiques and illustrate these with the aforementioned Gick and Holyoak
(1980) example. In the later sections we review advances in research on
transfer and emerging trends that attempt to address some aspects of these
critiques.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
CRITIQUE OF CLASSICAL VIEW
Three interrelated concerns emerged: the relation between context and
knowledge, the breadth of assessment, and the role of individual differences
in transfer. The classical view assumes that knowledge is separate (or
separable) from context. Explanations of transfer are typically based on
the type of features encoded from the learning problems (e.g., surface vs
structural) and how those relate to the test problem. Context is typically
limited to the learning and test tasks. For example, in Gick and Holyoak’s
experiment, transfer was analyzed in terms of the relationship between
the initial story problem and Duncker’s radiation problem. In contrast,
critics argue that context should include not only the learning task but also
the broader set of meaningful practices and activities in which that task is
situated, such as students solving homework problems as one part of a set
of interrelated class-based activities (Greeno, 2006; Lave, 1988). Critics also
argue that context should include both motivational aspects (e.g., goals and
incentives) as well as social aspects (e.g., the other people involved, status
of the evaluator), which have implications for how and why the tasks are
performed (Lobato, 2012; Pea, 1987).
The second concern is the breadth of measures to assess transfer. Most
dependent measures were problem-solving accuracy, solution strategy, or
reaction time, which is a relatively narrow band of behavioral measures. For
example, Duncker’s radiation problem was a single problem and was scored
as correct only if participants generated the expected convergence solution.
In contrast, critics argued that the acquired knowledge might be helpful for
learning from other tasks (Bransford & Schwartz, 1999).
The third concern is that this approach had given little attention to individual differences. Although some of the past research examined the effects of
expertise on transfer (Novick, 1988, 1992), most did not examine the effects
of individual differences, especially with respect to motivation, which
may be particularly important in some contexts over others. For example,
Gick and Holyoak did not examine individual differences in students’
prior knowledge or motivation for the task. Taken together, both the work
conducted within the classical paradigm and the critiques of that paradigm
have pushed research forward. We now describe three illustrations of this
forward progress.
ADVANCES RESULTING FROM THE TRANSFER DEBATE
A BETTER UNDERSTANDING OF THE LEARNING PROCESSES THAT FACILITATE TRANSFER
One set of advances resulting from the research in the classical paradigm
is a better understanding of what learning processes facilitate transfer. In
Knowledge Transfer
5
fact, several of the initial papers reporting transfer failures led to subsequent
work examining which types of instruction could better promote transfer
in those scenarios. Much progress was made in understanding the role of
self-explanation and analogical comparison in facilitating abstraction, understanding, and transfer. Other work has focused on the nature of the learning
material, including research on using worked examples (Atkinson, Renkl, &
Merrill, 2003; Paas & Van Merriënboer, 1994), the role of variability in practice
(Chen, 1999; Nokes & Ohlsson, 2005), and whether the learning content was
concrete or idealized (Goldstone & Sakamoto, 2003; Goldstone & Son, 2005;
Sloutsky, Kaminiski, & Heckler, 2005). Here, we focus on two illustrations
of these advances; see Koedinger, Corbett, and Perfetti (2012) for a broader
review.
The first advance is research on self-explanation. Self-explanation is the
process of explaining to oneself with the goal of making sense of new information. A number of laboratory studies have shown that self-explanation
can promote learning and transfer. Correlational studies have shown that
successful students self-explain when learning new information in domains
such as biology, physics, and mathematics and then use that knowledge
to solve novel problems and answer deep questions (Chi, Bassok, Lewis,
Reimann, & Glaser, 1989). Several experiments have also demonstrated
a causal relationship, showing that prompting self-explanations during
learning tasks promoted transfer to new problems (Atkinson, Renkl, &
Merrill, 2003; Chi, de Leeuw, Chiu, & LaVancher, 1994). Self-explanation
is hypothesized to work through generating inferences, in which students
can connect their prior knowledge to the new topic, and by providing an
opportunity to identify and revise misconceptions (Chi, 2000).
The second example of a learning process that facilitates transfer is analogical comparison. Much research has shown that the act of comparing and
contrasting cases or examples by aligning similarities across the examples
can help students learn the common structure between the two examples (for
a review, see Alfieri, Nokes-Malach, & Schunn, 2013). The resulting knowledge is sometimes referred to as an abstract schema, as it is hypothesized to
store the common structural features between the two examples and discard
the nonoverlapping superficial features. The resulting schema can facilitate
transfer to problems with different surface features than the learning task
but with similar structure (Novick & Holyoak, 1991; Reeves & Weisberg,
1994). Schema theory provides a representational vehicle to explain how the
acquired knowledge can support transfer. As two separate cognitive pathways to transfer, self-explanation and analogical comparison are representative of the broader classical effort to identify the instructional conditions and
the underlying cognitive processes that support transfer.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
A BETTER UNDERSTANDING OF THE APPLICATION PROCESSES THAT ENACT TRANSFER
In the previous section we focused on instruction and learning processes
hypothesized to generate knowledge that transfers. In this part we briefly
describe research that has identified different types of transfer application
mechanisms. Each of these mechanisms takes some type of knowledge
acquired from the learning task and then applies it to the test. The mechanisms include rule transfer (Singley & Anderson, 1989), analogy (Gentner,
1983; Gick & Holyoak, 1980), knowledge compilation (Anderson, 1987), and
constraint violation (Ohlsson, 1996). Each mechanism has been theorized
to use a set of qualitatively distinct cognitive processes, require different
amounts of computation, and operate on different types of knowledge
structures (e.g., analogy uses problem exemplars, whereas constraint violation uses principle constraints). Each mechanism has received independent
empirical support for its existence and each has been implemented as a
computational model (see Nokes-Malach & Mestre, 2013 for a summary
of the mechanisms). However, relatively little work has compared and
contrasted these mechanisms to another within the same experimental
paradigm.
Recently we compared the predictions of three of the mechanisms (analogy, knowledge compilation, and constraint violation) to one another in a
laboratory study on transfer (Nokes, 2009). The results showed that each
mechanism was distinct and identifiable and was triggered under different
conditions depending on the participants’ prior knowledge and the features
of the transfer task. This investigation into the boundary conditions of each
mechanism has begun to shed some light on the conditions that underlie successful transfer as well as why transfer might fail under some learning and
test situations (Nokes & Belenky, 2011; Nokes-Malach & Mestre, 2013).
A BETTER UNDERSTANDING OF THE (MIS)MEASUREMENT OF TRANSFER
A third major advance is the development of measurement innovations to
better understand transfer phenomena. One recent taxonomy of transfer proposed by Barnett & Ceci (2002) aims to better conceptualize the transfer distance as a function of context and content. Context focuses on when and
where knowledge is transferred along dimensions of time, modality, function, knowledge domain, physical space, and social setting. Content focuses
on what is transferred, from near transfer of executing procedures to intermediate transfer of adapting procedures to far transfer of recognizing and relating concepts or principles to one another and to new problem features. This
taxonomy affords both researchers and educators an opportunity to define
and identify different types of transfer. No longer should researchers use a
single term for describing transfer outcomes, but should instead use specific
Knowledge Transfer
7
terms regarding transfer content and context. This may also help in understanding the mixed results in the literature. For example, different types of
instructional conditions and learning processes may be better suited to support different types of transfer, and some types may have more support than
others. The aforementioned work investigating which types of transfer processes are enacted depending on prior knowledge and features of the transfer
problem may lead to further insights as to what types of transfer outcomes
each process predicts.
TRANSFER AS PREPARATION FOR FUTURE LEARNING
A second measurement advance is the experimental formalization of the
concept of preparation for future learning (PFL) (Bransford & Schwartz,
1999). PFL adds an important middle step to the classical paradigm: After
some initial learning experience, the individual is given an additional
learning opportunity or resource followed by the novel task. The idea is
that the learning task creates some initial knowledge and, although this
knowledge does not lead to direct improvement on the transfer problem,
it “prepares” the learner for learning from the additional resource. The
knowledge acquired from the learning resource then supports transfer. This
broadens the definition of transfer and better captures some real-world
transfer problem scenarios. For example, instructing students to read an
assigned text before a lecture could impact what and how they learn from the
lecture (learning resource), which could then affect later test performance.
See Figure 1 for an illustration of this paradigm. One line of research using
this paradigm has shown that invention activities (e.g., students attempted
to discover statistical techniques to account for some observed data) better
prepared students to learn from a new learning resource (e.g., a worked
example) than more traditional tell-and-practice instruction (Schwartz &
Martin, 2004).
RECENT ADVANCES IN THE INVESTIGATION OF MOTIVATION AND TRANSFER
There has been relatively little prior work relating individual differences
(e.g., developmental differences, cognitive factors, and motivation) and
transfer. Here, we focus on individual differences in motivation because we
believe it connects well to the context critique in that different types of contexts have implications for participants’ motivation. Although motivation
has been theorized to be an important factor for transfer, there has been
little empirical work examining this connection until recently (e.g., Harp
& Mayer, 1998). One motivational construct that prior work suggests may
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Learning treatment A
Learning treatment B
Common learning
resource
Target transfer problem
Figure 1 Preparation for future learning research paradigm, in which initial
learning experiences prepare students to learn from a new resource, which in turn
provides knowledge that can be transferred to a new problem. Adapted from
“Inventing to Prepare for Future Learning: The Hidden Efficiency of Encouraging
Original Student Production in Statistics Instruction,” by D. L. Schwartz and T.
Martin, 2004, Cognition and Instruction, 22(2), p. 147. Copyright 2004 by Taylor
and Francis Ltd.
be particularly relevant to transfer is students’ achievement goals (Pugh &
Bergin, 2006).
Achievement goals are the reasons people have for engaging in
competence-based achievement activities, similar to those pursued in
school (Ames & Archer, 1988; Dweck, 1986; Elliot & McGregor, 2001). One
dominant theory of achievement goals is Andrew Elliot’s 2×2 conceptual
framework that specifies two dimensions of goals based on their definition
and valence (Elliot & McGregor, 2001). Definition refers to whether the goal
is focused on developing competence in comparison to an intrapersonally
defined expectation or prior competence (mastery) or in comparison to
others (performance). Valence refers to whether the goals are focused
on seeking positive outcomes (approach) or averting negative outcomes
(avoidance). Combining these two dimensions results in four types of
goals: mastery-approach, mastery-avoidance, performance-approach, and
performance-avoidance.
Each goal has been associated with different behaviors, affects, and achievement outcomes. Mastery-approach goals have been associated with positive
outcomes such as better self-regulation, deeper cognitive strategies, more
effort, and persistence in the face of challenges (Elliot, McGregor, & Gable,
1999). However, these goals only occasionally predict achievement outcomes
as measured by grades. Performance-approach goals have been associated
with good grades in school (Linnenbrink-Garcia, Tyson, & Patall, 2008), but
they are also associated with superficial learning strategies (Elliot, McGregor,
& Gable, 1999). Performance-avoidance goals have been associated with uniformly bad outcomes, poor achievement, test anxiety, and reduced interest
(Elliot & McGregor, 1999; Elliot & Harackiewicz, 1996). Mastery-avoidance
Knowledge Transfer
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goals have been empirically examined only recently, but there is evidence
that they are associated with positive behaviors, such as help-seeking, as
well as negative behaviors, such as procrastination (Howell & Watson, 2007;
Roussel, Elliot, & Feltman, 2011).
Recently, we have begun to explore the relationship between students’
achievement goals, instruction, and transfer (Belenky & Nokes-Malach,
2012, 2013; Nokes & Belenky, 2011; Richey & Nokes-Malach, 2013). In two
experiments, Belenky and Nokes-Malach (2012, 2013) found that students
who reported high levels of dispositional mastery-approach goal orientation
for mathematics were more likely to learn and transfer new statistics
concepts than students who reported low levels of mastery-approach
orientation. In contrast, none of the other achievement goals were predictive
of transfer. Furthermore, we examined whether mastery goals could be
promoted with invention activities. We found that students who were
given invention activities followed by direct instruction were more likely
to adopt state-based mastery goals during learning activities than students
who were first given direct instruction followed by practice. Furthermore,
this invention activity moderated the effect of students’ dispositional
mastery-approach orientations on transfer, such that students who entered
the experiment low in initial mastery-approach goals were more likely
to transfer in the invention condition than in the practice condition. We
speculate that mastery-approach goals facilitated constructive cognitive
processes such as analogy or self-explanation during learning and thereby
created abstract knowledge that transferred. It is also possible that these
goals helped during the test by encouraging students to actively seek out
and make connections with prior knowledge. By exploring the connections
between achievement goals and related cognitive processes, we move closer
to developing a more comprehensive model of transfer (Nokes-Malach &
Mestre, 2013).
FUTURE DIRECTIONS
In sum, although transfer is clearly a complex and multifaceted phenomenon,
much progress has been made. The classical research paradigm has produced
many advances in our understanding of the instruction, learning, and application processes that facilitate different types of transfer. We highlighted two
learning paths of self-explanation and analogical comparison that have been
shown to facilitate the far transfer of content. Future work should analyze
what is known about these and other paths in order to construct a more general model of transfer.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
We also described research examining the different cognitive mechanisms
of knowledge application (e.g., analogy, knowledge compilation, and constraint violation). Recent work has found some initial evidence that these
mechanisms are triggered for different types of transfer scenarios (i.e., prior
knowledge and task features) and lead to different transfer outcomes (Nokes,
2009). We hope new research continues to test and refine these hypotheses as
well as develop computational models of these mechanisms within the same
cognitive architecture to further explore the relationships between them.
The field has also made progress in defining transfer. We believe that some
of the enduring controversy can be traced to using a single term to refer to
an array of different transfer processes and outcomes. Taxonomies such as
Barnett and Ceci’s can facilitate future progress by providing a shared terminology for researchers to better define and measure transfer phenomena. We
also hope that researchers use these definitions to conduct meta-analyses of
the existing literature to examine the relationships between learning variables, contexts, and types of outcomes. This taxonomy should be used in
future experiments to specify more precisely the transfer outcomes and guide
study to underexplored areas. Importantly, recent work has also begun to
explore other experimental paradigms such as PFL (Bransford & Schwartz,
1999). The PFL approach has led to a broadening of the definition and measurement of transfer. Other types of transfer scenarios should be developed
and explored.
Future work should also further examine individual differences in transfer.
This critique prompted us to consider the effect of motivational variables
on transfer. We have shown that there are strong relationships between
mastery-approach achievement goals and transfer. However, there is much
work to be done to test the relationship between these goals and cognitive
processes. For example, do mastery-approach goals facilitate spontaneous
analogical comparison or self-explanation? A major challenge for future
work will be to develop a theory of transfer that connects cognitive factors
and mechanisms to the broader instructional, motivational, and social
aspects of the transfer context.
ACKNOWLEDGMENTS
This work was supported by Grant SBE-0836012 from the National Science
Foundation, Pittsburgh Science of Learning Center (http://www.learnlab.
org). No endorsement should be inferred. We thank members of Cognitive
Science Learning Laboratory and Sarah Nokes-Malach for their helpful comments and suggestions on the paper.
Knowledge Transfer
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TIMOTHY J. NOKES-MALACH SHORT BIOGRAPHY
Timothy J. Nokes-Malach is an Associate Professor of Psychology and a
Research Scientist at the Learning Research and Development Center at the
University of Pittsburgh. He received his Bachelors degree from the University of Wisconsin-Whitewater, PhD from the University of Illinois at Chicago,
and Postdoctoral training at the Beckman Institute for Advanced Science and
Technology at the University of Illinois at Urbana-Champaign. His research
focuses on human learning, problem solving, and knowledge transfer, and
most recently on the interactive effects of motivation and social interaction
on those processes. His work has been supported with grants from the Pittsburgh Science of Learning Center, the National Science Foundation, and the
Department of Education’s Institute for Education Sciences.
http://www.lrdc.pitt.edu/nokes/CSL-lab-home.html
J. ELIZABETH RICHEY SHORT BIOGRAPHY
J. Elizabeth Richey is a graduate student in the cognitive psychology program at the University of Pittsburgh. She received her Bachelors degree from
the University of Pittsburgh. Her research explores conceptual learning,
problem solving, analogical reasoning, motivation, and the relationship
between existing knowledge and future learning. She is interested in the
implications of cognitive psychology for math and science instruction.
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