Journal of Engineering Education
January 2011, Vol. 100, No. 1, pp. 151–185
© 2011 ASEE. http://www.jee.org
Situated Engineering Learning:
Bridging Engineering Education Research
and the Learning Sciences
ADITYA JOHRI AND BARBARA M. OLDSa
Virginia Tech, U. S. National Science Foundationa
CONTRIBUTORS
Indigo Esmonde, University of Toronto; Krishna Madhavan, Purdue University; Wolff-Michael Roth,
University of Victoria; Dan L. Schwartz and Jessica Tsang, Stanford University; Estrid Sørensen,
Humboldt University and Aarhus University; Iris Tabak, Ben Gurion University of the Negev
BACKGROUND
The field of engineering education research has seen substantial growth in the last five years but it often
lacks theoretical and empirical work on engineering learning that could be supplied by the learning sciences. In addition, the learning sciences have focused very little on engineering learning to date.
PURPOSE
This article summarizes prior work in the learning sciences and discusses one perspective—situative learning—in depth. Situativity refers to the central role of context, including the physical and social aspects of
the environment, on learning. Furthermore, it emphasizes the socially and culturally negotiated nature of
thought and action of persons in interaction. The aim of the article is to provide a foundation for future
work on engineering learning and to suggest ways in which the learning sciences and engineering education research communities might work to their mutual benefit.
SCOPE/METHOD
The article begins with a brief discussion of recent developments in engineering education research. After
an initial overview of the field of learning sciences, situative learning is discussed and three analytical
aspects of the perspective are outlined: social and material context, activities and interactions, and participation and identity. Relevant expert commentaries are interspersed throughout the article. The article
concludes with an exploration of the potential for contributions from the learning sciences to understanding engineering learning.
CONCLUSION
There are many areas of mutual benefit for engineering education and the learning sciences and many
potential areas of collaborative research that can contribute not only to engineering learning but to the
learning sciences.
KEYWORDS
engineering learning, learning sciences, situative learning
INTRODUCTION
In recent years, there has been a concern about the need to develop a better understanding of how people learn engineering. Previous efforts to improve engineering education
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have often followed an ad-hoc path without a systematic understanding of how learning
occurs and without the development of a body of knowledge upon which to build. To help
redress this situation, we review scholarship on learning broadly and engineering learning
specifically with the aim to build a framework that can guide future research on engineering learning. This article complements other articles in this issue (Adams et al., 2011;
Borrego & Bernhard, 2011; Litzinger, Hadgraft, Lattuca, & Newstetter, 2011) that also
focus on learning.
We begin with a brief review of the history of engineering education research. We then
review research on learning more generally and survey the field of learning sciences. As an
exemplar, we focus on one dominant perspective on learning in that field, situativity, and
draw some implications of this perspective for research on engineering learning. Next, we
discuss how learning sciences and engineering learning can mutually inform each other, in
particular what engineering education researchers can learn from scholarship in the learning sciences and what fundamental contributions engineering can make to research on
learning. Interspersed throughout the article are research commentaries we have invited
from six established and emerging international scholars in the learning sciences. Our hope
is that the engineering learning community will see common ground and common questions in these commentaries and increase their familiarity with the learning sciences. Finally, we end with a short discussion and conclusion section that summarizes the key points of
the article as well as outlines some areas for development.
From the perspective of engineering education, two significant differences exist
between engineering learning and research in the learning sciences. First, learning
sciences researchers have traditionally focused primarily on issues related to K-12 education; even though research on K-12 is on the rise within the engineering education community, such studies are still quite limited compared to those focusing on
undergraduate teaching and learning. Still, fundamental ideas about learning apply
irrespective of the targeted user population and will be useful for engineering learning
research. Second, learning sciences research has not focused much on engineering
disciplines. As a result, in some sense this is a great opportunity for engineering
educators to make a significant contribution to the field. As we argue, a movement
parallel to the rise of situative learning in the learning sciences is on the rise within
engineering education; more and more scholars are engaged in research on engineering learning, and engineers and engineering have become the focus of novel and exciting research on learning.
Our thesis is that the learning sciences and engineering learning potentially have
much in common, though the two communities have not interacted extensively up to
now. We are not arguing that collaboration with the learning sciences is the only direction in which engineering education research should go, nor that there has been no collaboration between the two communities until now, but rather that such collaboration is
important and promising and has been underexplored. Before we can examine the present state of research on engineering learning, it will be helpful to briefly discuss some of
its historical path.
ENGINEERING EDUCATION RESEARCH
Although much of the history presented here is focused on developments in the
U.S., engineering education research is clearly a global enterprise, as indicated by the
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contributors to this article, this issue of JEE, other journals focused on engineering education in many countries, and mechanisms such as the Research on Engineering Education Symposia that connect scholars with an interest in engineering education from
around the globe. The article by Borrego and Bernhard in this issue discusses in some
detail the emergence of engineering education research as an internationally connected
field of inquiry (Borrego & Bernhard, 2011). In addition, engineering education research has been fortunate to have more mature models in science and mathematics education from which it has learned much about the formation of a discipline. For example, Peter Fensham’s (2004) work on the evolution of science education as a research
field has influenced engineering education researchers as they consider the identity of
their new discipline.
Much of the early research in engineering education focused, understandably, on curriculum and instruction, the majority of it conducted by faculty whose professional training
was in an engineering discipline and who were responding to what they observed in their
engineering classrooms. John Heywood’s 2005 compendium contains much of this history
from a broad international perspective and summarizes ongoing lines of engineering education research with chapters on topics such as Concepts and Principles; Learning Strategies and Learning Styles; Human Development; Problem Solving; Design; and Assessment and Evaluation (Heywood, 2005). Some examples of the types of “effective learning
experiences” highlighted in Heywood’s book are also enumerated in the article by
Litzinger et al. in this issue (2011). However, there are relatively few examples from the
learning sciences per se, an indication perhaps of both the relatively recent emergence of the
learning sciences and their even more recent adoption within the engineering learning
community.
Pea and Collins (2008) identify four waves of science education reform activity over
the past half century and argue that the reforms are building towards a “cumulative improvement of science education as a learning enterprise” (p. 3). They identify these
waves as the post-Sputnik focus on curriculum reform in the 1950s and 1960s, the
focus on cognitive science studies of learners’ reasoning in science in the 1970s and
1980s, the standards movement in the late 1980s and 1990s, and the current focus on a
systemic approach to designing learning environments. Their metaphor of waves of reform is apt for understanding developments in the field of engineering education over
the past century. In a 2005 guest editorial for a special issue of JEE intended to “review
the current state of scholarship in key areas of engineering education,” Felder, Sheppard, and Smith take a similar approach to describing the waves of change in engineering education research. They argue that from the 1960s to the 1980s “engineering education journals and conferences remained focused on the mechanics of classroom
instruction with little regard for the science of education and little evidence of rigorous
scholarship” (p. 9). In the 1980s and 1990s, they continue, “scholarship in engineering
education began to move toward a new level of maturity and sophistication.”1 They
1
Before the time period covered by Felder, Sheppard, and Smith (2005), similar waves were present, as
identified by Seely (1999), of which the wave of engineering science has had the strongest hold on
engineering education. This movement was pioneered by engineers who moved to the U.S. from Europe
and developed a stronghold in American engineering colleges in the years immediately after World War II.
Interestingly, in his article Seely (1999) highlights that the actual intentions of reform seekers did not materialize as envisioned in the reforms that were implemented and argues that this gap between vision and
implementation in practice is persistent across waves of reform.
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conclude that going forward, engineering education will require “research guided by
theories grounded in cognitive science and educational psychology and subjected to
the same rigorous assessment and evaluation that characterize first-rate disciplinary
research” (p. 9). They also argue for partnerships among psychologists, social scientists, and engineering educators. Reform towards research has gained significant momentum in the past decade and it is within this context that this paper and this issue
are framed.
Clearly, engineering education research is a field of increasing prominence, global
reach, and theoretical grounding. We applaud the multiplicity of approaches taken and the
partnerships that are being developed. In the remainder of this paper, we hope to make the
case for the value of partnerships and collaborations between the learning sciences and engineering learning.
AN INTRODUCTION TO LEARNING
Over the past couple of centuries, scholars from a wide range of disciplines including philosophy, psychology, anthropology, and sociology have spent considerable time
trying to answer questions related to learning such as: How does cognitive development take place? How do we grow from a child with rudimentary abilities and knowledge into a highly skillful adult? How are humans able to engage in highly complex activities? Some scholars whose work has had a major influence on research on learning
include Lev Vygotsky (1962, 1978), Jean Piaget (1952, 1964), John Dewey (1896,
1934), Harold Garfinkel (1967), William James (1890/1950), George Herbert Mead
(1934/1962), Gregory Bateson (1978), Michel Polanyi (1967) and Jerome Bruner
(1960, 1990). Core ideas of these scholars adopted by learning researchers in their intellectual and methodological trajectory include Vygotsky’s socio-cultural learning and
zone of proximal development, Piaget’s genetic epistemology, Dewey’s transactional
account, James’s pragmatism and realism, Polanyi’s tacit knowledge, and Garfinkel’s
ethnomethodology. These ideas have not only shaped theoretical development of the
field of learning but have also influenced the design of learning environments including our schools and curricula. Many central ideas that we take for granted in educational practice, such as the progression of child development through specific stages
and the value of group work and collaborative learning, can be traced back to these influential scholars.
Greeno, Collins, and Resnick (1996), provide a useful classification of research on
learning that occurred over the twentieth century and divide the work into three broad
areas: behaviorist, cognitive, and situative.2 Behaviorism, a movement associated
primarily with its most prolific and controversial scholar, B. F. Skinner, “took the position that knowing could be characterized only in terms of observable connections
between stimuli and responses” (Greeno, Collins & Resnick, 1996, p. 16). For behaviorists, learning was the formation, strengthening, or weakening of those connections
2
There are many other aspects of learning as well, for instance, biological where muscle memory is critical; neuroscience research is shedding more light on these issues and their relationships with social aspects of learning (Meltzoff et al., 2009; also see Tomasello, 1999, and Tomasello & Call, 1997; to learn
more about the promise and perils of neuroscience research for education see commentary by Schwartz
and Tsang).
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through reinforcement. In other words, if a certain signal, such as ringing a bell, could
induce a particular action, such as taking a break from working on the computer,
learning had taken place. Furthermore, for behaviorists the mind (or brain) was a
black box and they were content with forgoing understanding how the mind works.
On the other hand, for the cognitive scientists an understanding of how the mind
works was central to their approach to learning as they were particularly interested in
how information was represented and processed by humans. Knowledge was seen as a
structure consisting of different concepts, and learning was the acquisition of abilities
such as reasoning, planning, solving problems, and comprehending language (Anderson, 1981, 1976; Newell & Simon, 1972). Unlike the behaviorists, for whom external
motivation was the driver for learning, the cognitive perspective was interested in
turning extrinsic motivation into intrinsic motivation. The cognitive movement got a
particular boost with the advent of computation and computers as these devices incorporated many of the conceptual features of cognition emphasized by cognitive scientists and also allowed them to model their own conceptions of learning. A central
attempt was to use the computer as a representation of how the mind works and to be
able to understand learning by developing the brain. The third perspective, situative,
views knowledge “as distributed among people and their environments, including
objects, artifacts, tools, books, and the communities of which they are a part” (Greeno,
Collins & Resnick, 1996, p. 17). In this view, learning is seen as meaningful participation in a community of practice with an understanding of “the constraints and
affordances of social practices and of the material and technological systems of environments” (p. 17). As can be seen, the situative movement differs significantly from
the other perspectives in its emphasis on the role of the environment on an individual’s
conception of knowing and how they learn; knowledge is not something an individual
possesses or stores in the brain but is present in all that they do. Clancey (1997b) succinctly summarizes the situative perspective and how it differs from the cognitive perspective when he argues, “The idea that knowledge is a possession of an individual is
as limited as the idea that culture is going to the opera. Culture is pervasive; we are
participating in a culture and shaping it by everything we do. Knowledge is pervasive
in all our capabilities to participate in our society; it is not merely beliefs and theories
describing what we do” (p. 271).
A further discussion of these ideas, including their implications for design of
learning environments, is available in Greeno, Collins, and Resnick (1996) and has
been summarized in Table 1 (also see Derry & Steinkuehler, 2003 for a short summary of the ideas). In addition, a series of articles and debates that appeared in the
journals Educational Researcher and Cognitive Science do an excellent job of summarizing various perspectives associated with situativity and concerns cognitive scientists
have with the concept (Brown, Collins, & Duguid, 1989; Greeno, 1989, 1997;
Norman, 1993a; Vera & Simon, 1993). Brief historical discussions of situated cognition can also be found in Bruner (1990) and Clancey (1997a, 2009). Recently, many
scholars have argued for research that bridges the cognitive and situative perspectives
on learning (Greeno & van de Sande, 2007; Vosniadou, 2007). These efforts are driven by a need to overcome what some see as a dichotomy between the acquisition
(learning is something we acquire, the cognitive perspective) and participation
(learning is participating, the situative perspective) metaphors (Sfard, 1998). Cognitive scientists argue that if all learning is situated and participatory, then how do we
account for transfer (Bransford & Schwartz, 1999)? The situativists answer this
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TABLE 1
Tabulated Summary of Greeno, Collins, and Resnick (1996)
criticism by arguing that we apply what we know in a new activity based on features
common to that activity and previous activities and by reframing the learning contexts (Engle, 2006). Still, this answer does not satisfy all conditions of transfer, such
as a novel domain or situation, and remains a critical challenge for the situative perspective. Arguing that both acquisition and participation metaphors can provide useful guidance for research, some scholars have proposed a third metaphor “knowledge
creation” as a way to provide a better overall framework, consisting of all three
metaphors, to advance our understanding of learning (Paavola, Lipponen, &
Hakkarainen, 2004). Another thorny issue between the proponents and opponents of
the situative perspective is the question of whether individuals learn or learning is a
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characteristic of an activity system (Salomon & Perkins, 1998). Although situativity
scholars argue that situativity does not preclude learning from occurring at an individual level, empirical studies of situated activity have primarily dealt with a grouplevel analysis.
A Brief Note on Critical Perspectives on Learning
Before moving on the to the next section, we want to briefly introduce the critical
perspective on learning which has shaped the thinking of learning researchers and is not
captured by any of the ideas discussed so far. Critical theorists argue for a re-examination of the
current state of affairs regarding how we teach (design of instruction), what we teach (design of
curriculum), and how we have reached the status quo (scrutiny of existing power relations that
shape learning). “Schools, in these perspectives, are seen merely as instructional sites. That they
are also cultural and political sites is ignored, as is the notion that they represent arenas of
contestation and struggle among differentially empowered cultural and economic groups”
(Giroux, 1983, p. 3). Critical theorists argue that fundamental change towards a more
equitable society and learning can occur only by looking at things from a fresh perspective. In
its most extreme form, some critical theorists argue for complete separation from the
establishment or the modern enterprise which is significantly reflected in current educational
settings.
In addition to the critical lens they bring to educational thinking, critical theorists also
espouse a participatory and reflective view of research. “The interface between theory and practice...is at a point where these various groups come together and raise the fundamental question of how they may enlighten each other, and how through such an exchange (of theoretical
positions) a mode of practice might emerge in which all groups may benefit” (Giroux, 1983,
p. 240). One research method that takes these concerns into account is action research, “Action
research is simply a form of self-reflective enquiry undertaken by participants in social
situations in order to improve the rationality and justice of their own practice, their understanding of these practices, and the situations in which the practices are carried out” (Carr &
Kemmis, 1986, p. 162). Though a detailed discussion of critical theory and action research is
beyond the scope of this article, we urge interested readers to explore it further in the works
cited previously and through Reason and Bradbury (2008) and Whyte, Greenwood, & Lazes
(1989), among others. Action research is also discussed by Case and Light (2011) in their
article in this issue.
THE LEARNING SCIENCES
Since the late 80s and early 90s many scholars interested in learning issues have coalesced around an emerging field called the Learning Sciences. In addition to the traditional scholarly disciplines, the learning sciences include scholars who self-identify with
artificial intelligence, cognitive science, computer science, organizational science, educational sciences, information sciences, linguistics, and neurosciences. The institutionalization of the field had its beginnings in the first conference in the field (the International
Conference of the Learning Sciences) and the subsequent journal, the Journal of the
Learning Sciences, which published its first issue in 1991. The Institute for Learning
Sciences was opened at Northwestern University in 1989 followed by the first doctoral
program in learning sciences. The International Society for the Learning Sciences
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was officially established in 2002 and in addition to its largely U.S.-based members, the
leadership has made significant efforts to attract members from all over the world.
Subsequent conferences of the society have been held in Taiwan, Greece, the
Netherlands, and Chicago.3 In recent years the community has become increasingly
international with scholars from Europe and Asia playing a significant leadership role
in the society and its journals. Still, the majority of research we review here comes
from U.S. scholars and follows a tradition of examining learning within K-12 settings.
Recent work that examines higher education settings has started to emerge in Europe
and has been reviewed in the Case and Light (2011) paper in this issue, particularly
under the section on phenomenography (e.g., Bruce, Pham, & Stoodley, 2004;
Carstensen & Bernhard, 2009; Ebenezer & Fraser, 2001, Swarat, Light, Park, &
Drane, in press).
Overall, learning sciences researchers study learning in diverse contexts using a variety of methodologies ranging from ethnographic field studies to lab-based investigations. In addition to theoretical development, scholars apply their findings to design
novel learning environments. Although the contributions of scholars in the field have
been wide ranging, some topics relevant for engineering education that have been studied include learning by analogy, scientific reasoning and discourse, apprenticeship, intentional learning, collaboration, transfer, design, imitation, guided discovery, anchored
instruction, and modeling (e.g., Barron, 2003; Bereiter & Scardamalia, 1989; Brown &
Campione, 1994; CTGV, 2000). Learning science scholars have developed and tested
new models of instruction including problem and project-based learning (Barron et al.,
1998), case-based reasoning (Kolodner et al., 2003), and adaptive expertise (Hatano &
Inagaki, 1986). The study of collaboration in both face-to-face small groups (Barron,
2000) and computer-mediated settings (Rummel & Spada, 2005) has received significant attention by learning sciences researchers. Another area that has garnered much
interest from the learning sciences community is how the use of technology shapes
learning. This has included investigating the role of scientific visualizations, simulations,
and modeling for teaching science and mathematics Gordin & Pea, 1995; Roschelle,
1992; Roschelle & Pea, 2002.
In addition to content-agnostic principles of learning, disciplinary research on science
and mathematics learning forms a core contribution of scholars (Cobb, Yackel, &
McClain, 2000; diSessa, 1982; Saxe & Guberman, 1998). Many scholars have written on
learning in the disciplines such as physics (Chi & VanLehn, 1991; diSessa, 1982; Etkina
et al., 2010), chemistry (Kozma, Chin, Russell, & Marx, 2000), and mathematics (Boaler
& Greeno, 2000; Sfard & McClain, 2002), which are of special interest to engineering educators. Engineering education researchers have been fortunate to have existing models
and theories to borrow from in related fields such as science education and mathematics
education. These models have helped engineering education researchers think about questions related to the identity of the field as well as borrow theoretical and methodological
3
Although, as with most fields and disciplines, it is hard to come up with a definition of learning sciences,
the official society’s Web site (www.isls.org ) lists its mission as: “Learning Sciences (LS) investigations include fundamental inquiries on how people learn alone and in collaborative ways, as well as on how learning
may be effectively facilitated by different social and organizational settings and new learning environment
designs, particularly those incorporating information and communication technologies (ICT), as in computer supported collaborative learning (CSCL).” Another perspective is available in Nathan & Alibali
(2010).
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models. Several examples of how learning research in the disciplines has shaped engineering education can be cited. Perhaps the most viral of these has been the development of
concept inventories in a variety of engineering topics. As described in the article by
Litzinger, Lattuca, Hadgraft, and Newstetter (2011) in this issue the Force Concept Inventory (FCI) developed by Hestenes and Hake has been influential in the development of
multiple inventories in engineering topics. According to Streveler et al., “learning conceptual knowledge in engineering science is a critical element in the development of competence and expertise in engineering. To date, however, research on conceptual learning in
engineering science has been limited” (Streveler, Litzinger, Miller, & Steif, 2008, p. 279).
They demonstrate the synthesis not only of an idea (concept inventories) that grew out of
physics but also the way in which fundamental research by cognitive psychologists can inform engineering learning research. In the area of mathematics education, one area of research that has gained some traction in engineering is model eliciting activities(MEAs).
MEAs use open-ended case studies to simulate authentic, real-world problems that small
teams of students address (Hamilton, Lesh, Lester, & Brilleslyper, 2008; Yildirim,
Besterfield-Sacre, & Shuman, 2009; Zawojewski, Diefes-Dux, & Bowman, 2008).
Originally designed to observe the development of middle school student problem-solving
competencies and the growth of mathematical cognition, it soon became apparent that
MEAs provided a methodology to help students become better problem solvers “Lesh,
2003”.
In addition to theoretical development, learning sciences scholars have also explored
innovative ways of undertaking research such as design-based research, founded on the idea
of design experiments (Brown, 1992; Collins, 1992; Design-Based Research Collective
(DBRC), 2003), and methodological approaches such as interaction analysis (Jordan &
Henderson, 1995) and video analysis (Derry et al., 2010) form a core contribution of the
field. Learning sciences scholars have also made significant efforts to bridge the gap between
learning research and educational practice (Bransford, Derry, Berliner, Hammerness, &
Beckett, 2005; CTGV, 1997) through the design of learning environments.
Of course, like most academic disciplinary fields, the learning sciences community itself
has a dynamic identity often in a state of flux. In recent years several community members
have questioned the purpose, membership, and role of the organization in several forums and
have directed their research resources towards sense-making around the community through
interactive symposia (Evans et al., 2010) and reflective and summative reviews (Nathan &
Alibali, 2010). For a further understanding of the work in the field, we refer readers to The
Cambridge Handbook of the Learning Sciences (2005). It is also important to mention that the
National Research Council’s report How People Learn (2000) was largely the product of a collaboration among learning scientists. Several recent review articles do an excellent job of providing a background of the field and laying out future directions for research (Bransford et al.,
2006; Clancey, 2009; Sawyer & Greeno, 2009). For instance, an emerging trend within the
field is an emphasis on “learning outside of school” given the research on after-school settings,
museums, digital media, community programs, and gaming (JLS, 2009, editor’s note) and
Bransford et al. (2006) argue that the next decade will see a synergy between neuroscience accounts of learning and research in formal and informal learning.4
4
Other venues for this research include the Journal of the Learning Sciences, International Journal of
Computer-Supported Collaborative Learning, Cognition and Instruction, Educational Psychology, Mind Culture
and Activity, and journals of the American Educational Research Association (AERA) and the European Association for Research on Learning and Instruction (EARLI), among others.
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THE SITUATIVE PERSPECTIVE ON LEARNING
As introduced above, one significant change in research on learning over the past couple of decades is a move towards examining learning as a situated activity. The situative
perspective is broad and owes a debt to many scholars and ideas. Its seeds can be traced
back to the works of Dewey (1934). This perspective has been referred to as situated cognition (Brown, Collins, & Duguid, 1989; Greeno, 1989), situated learning (Lave, 1991;
Lave and Wenger, 1991), situated action (Suchman 1987/2007), socio-cultural psychology (Rogoff, 1990; Wertsch, 1993), activity theory (Engeström, 1987), and distributed
cognition (Hutchins, 1995). Greeno (2006) refers to the perspective as situative and/or
situativity, as opposed to situated learning, to prevent the misconception that only some
action, cognition, or learning is situated. He argues, as do others (Lave, 1988; Suchman,
1987), that all action, cognition, and learning is situated, whether in informal settings or
formal school settings. The situative perspective views human knowledge as arising conceptually through dynamic construction and/or reinterpretation within a specific social
context (Clancey, 2009). Furthermore, knowledge is socially reproduced and learning occurs through participation in meaningful activities that are part of a community of practice
(Lave & Wenger, 1991), participation which is mutually constituted through and reflects
our thinking and literacy skills (Gee, 1997). According to Sawyer and Greeno (2009) the
core commitment of the situative perspective is, “to analyze performance and transformation of activity systems that usually comprise multiple people and a variety of technological
artifacts (p. 348).” In other words, a central aim of the situated perspective is to understand
learning as situated in a complex web of social organization rather than as a shift in mental
structures of a learner.5
We believe that a broader and deeper understanding of the situative perspective can
provide valuable lessons for engineering educators particularly in their efforts to develop
theoretical insights into engineering learning. To facilitate this process, we next outline
and discuss three analytical aspects of situative learning. First, we will look at the importance of the social and material context on learning. Second, we will examine the role of
activities and interactions in situated learning. Finally, we will explore the ideas of participation and identity in relation to situativity. After discussing each concept in detail, we will
explore their significance for engineering learning and how they can help inform future research. In addition, we have invited a group of international contributors to speak to these
ideas in more depth in their commentaries.
Social and Material Context
From a situated learning perspective, all learning takes place within some social and
material context. Humans are constantly surrounded by other people and by objects and
artifacts. Tools mediate all our activities including learning processes and therefore the
role and use of tools is important to understand. Tools not only have a physical dimension
5
In addition to the scholars cited above, readers can refer to the following edited volumes for in-depth work on
this topic: Perspectives on Socially Shared Cognition (Resnick, Levine & Teasley, 1991), Handbook of Situated Cognition (Robbins & Aydede, 2009), Mind, Culture & Activity (Cole, Engeström & Vasquez, 1997), Everyday
Cognition (Rogoff & Lave, 1984), Perspectives on Activity Theory (Engeström, Miettinen & Punamaki, 1999),
Communication and Cognition at Work Engeström & Middleton, 1998), Cultural Psychology (Cole, 1996),
Sociocultural Studies of Mind (Wertsch, Río, & Alvarez. (Eds.), 1995), Distributed Cognitions (Salomon, 1993),
and Situated Cognition: Social, Semiotic, and Psychological Perspectives (Kirshner & Whitson, 1997).
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but also allow us to represent symbols and manipulate those symbols.6 In Commentary 1,
Estrid Sørensen from Aarhus University in Denmark discusses her work on the materiality of learning and some possible implications for engineering education.
Commentary 1: The Materiality of Learning, Estrid Sørensen, School of Education,
Aarhus University and Humboldt University
Intense discussions have unfolded over the last decades concerning the place
of agency. Scholars of Science and Technology Studies have shown how materials
can come to act—often on human beings. A classic study is Latour’s description of
a hotel manager, who in vain keeps telling his guests to hand over the keys when
they leave the hotel (Latour 1992). Frustrated, he attaches to each key a heavy
weight, whereby he, in Latour’s terms, delegates the agency involved in telling
guests to hand over the keys to the weights. The agency of the weights turns out to
be more successful than the hotel manager’s agency: the guests now actually return
their keys!
Latour’s Actor-Network Theory is more sophisticated than emphasizing
the agency of things. Important is the approach to humans as well as nonhumans
as hybrids (Latour, 1993). The key weights were not entirely nonhuman since the
hotel manager’s human agency was folded into them. The agency of the weights
was a hybrid effect of the interrelation of hotel manager and weights. Likewise,
the docility of the guests dutifully leaving the keys on the reception desk was not
purely human. It was a hybrid result of the heavy weights and the guests’ distaste
for carrying weight around.
Engineering education is probably one of the most material-saturated
disciplines. For this reason, from an Actor-Network Theory perspective, even the
overview of materiality in this article falls short of the possibilities of materiality
since materials are only accounted for as a context for learning. Learning itself is
seen as a purely human endeavor. This oversight is not surprising since other than
in their role as tools and scaffolds materials are hardly to be found in learning
theory. How can we understand the specificities of learning engineering if we fail
to take into account the hybrid postures engineering students have to develop as a
result of the interaction of their bodies and the variety of apparatuses involved in
the craft of engineering? How can we understand collaboration between learners
of engineering if we ignore that these collaborations unfold around material
things? How much would there be left of learning engineering if all nonhuman
things were left out?
With such questions and such an approach, I set off to reconsider situated
learning theory (Sørensen, 2009). The basic point in this endeavor is to
conceptualize learning not as a product of social interaction, but as a hybrid,
socio-material process. Through classroom observations I learned that different
forms of knowledge emerged when different materials were involved. The size,
similarity and stability of the blackboard, the textbook and the layout of the
classroom contributed to a form of knowledge that implied that knowledge was
located in the heads of children, in the classroom situation, and in the book.
Compared to this, a much more fluid, processual knowledge emerged when the
6
The role of the context and its relation with materials has been salient in the work of scholars arguing for a
distributed cognition perspective (Hutchins, 1995, 1993; Norman, 1993b; Pea, 1993; Perkins, 1993).
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students engaged with a 3D virtual environment, a blog, and the Internet. It was
revealed that learning differed when different materials were involved, and thus
the evaluation of what was learned and the relevance of the learned varied. These
findings emphasize not only the socio-material character of learning, but also
learning’s translocal character: because materials, such as a book and a blog have
different extensions, the space of learning is sometimes bounded to a
geographically small area, the situation, and sometimes it is extended and
fluctuating. Accordingly, the understanding of learning as a socio-material
process also affects our concepts of the spatial and temporal dimensions of
learning.
Materiality is evident not just in physical objects or tools, but in representations as
well. In this manner, language becomes part of our ecology of learning, of our social and
literacy practices (Gee, 1992, 2003). From a situative perspective, representations get
their meanings from their use, “representations are as representations do (Dourish,
2001),” in contrast to the cognitive perspective where representations are useful in terms
of information processing. Representations are not just about disciplinary knowledge
but about other people as well. Our impressions of others are central to how we interact
with them and often, especially when digital materiality is involved, these impressions
are formed from interpretation of digital cues of others. In summary, the socio-material
context is central to understanding and designing learning from a situative perspective.
Representations have been a strong focus of research within the learning sciences and
have played an especially strong role in our understanding of science and mathematics
practices (Danish & Enyedy, 2006; Greeno & Hall, 1997; Hall, 1996; Lee & Sherin,
FIGURE 1. In this videotape offprint, composed by overlaying three images, a professor gestures while drawing and talking over an (incorrect) entropy-temperature
diagram as he teaches cooling by adiabatic demagnetization.
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2006; Zhang, 1997). Mediation and representational abilities have been shown to be
central in learning science and mathematics, especially for expertise development
(Pea,1993b). Wolff-Michael Roth’s work explores the importance of inscriptions in
Commentary 2.
Activities and Interactions
Context is meaningless for learning if a learner is not engaged in interactions and
activity—context is in the doing and being and is not a static environment (McDermott,
1999). According to the situative perspective, learning is doing (Greeno, 2005) and it is
through situated engagement in motivated action (Goodwin, 2000), using tools, and in
interaction with others, that we learn some of our most essential skills. For instance, a
child first acquires language through its use with parents. Clancey (1997b) has conceptualized an activity as a participation framework, “an encompassing fabric of ways of
interacting that shapes what people do (p. 266).” Activities are intentional actions and
they serve as a background for human actions such as deliberation, planning, description,
and so on. They are critical sense-making units as our interpretations of actions or artifacts, our perceptions and interpretations are situated within specific activities; an interruption has a different meaning when we are engaged in finishing a publication compared
to when we are watching television. The concept of activity is also a central tenet of
activity theory, a theoretical line of inquiry that has developed from ideas first stated by
Lev Vygotsky7 (1978, 1962) and expanded by Leont’ev (1978), Luria (1976), and, more
recently, Wertsch (1998) (also see commentary by Roth). According to this view, we are
motivated to engage in activities in part by our need to transform an object into a desired
outcome.8 Engeström (1987) calls activity, “the smallest and most simple unit that still
preserves the essential unity and integral quality behind any human activity” (p. 81).
Overall, mediation by tools and engagement in activities (that include social interaction)
are essential for learning and require paying attention to the micro-foundation of interaction. Interpretation embedded in action and interaction is also the underlying notion
behind the social constructivist view of the world which advocates understanding the fabric of interaction that has emerged over time through reciprocal roles played by actors
(Berger & Luckmann, 1966).
Commentary 2: Inscriptions: A Cultural-Historical Activity Theoretic Approach to
Scientific Representation, Wolff-Michael Roth, University of Victoria
We live in a visual culture. It is therefore perhaps not surprising that one of
the pervasive features of science, engineering, and technology is their use of visual
representations in the form of graphs (Figure 1), photographs, drawings, diagrams,
tables, or equations (Roth, 2003). On average, research shows that there are around
two visual representations per research article in the sciences. In fact, the history of
imagery in the sciences suggests that without visuals, the sciences, engineering, and
technology would not be what they are today. The visual representations, the
7
Vygotsky (1978) essentially argued that all higher order psychological functions, including learning, first
emerge on a social or interpersonal plane and are then internalized (move to an intra-personal plane). This
transition occurs in interaction with others who are at a higher plane than us and through mediation by
tools (artifacts, language).
8
Ideally, the activities are motivated at a higher socio-historical level, an idea better captured by the participation metaphor of the community of practice perspective (also see Commentary 2 by Roth).
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history of the sciences, and the cultural ways of communicating within and about
science are but different sides of the same phenomenon. Visual representations also
are important in teaching: There is not a day in science teaching at all ages where
students would not see the instructor use some visual representation; and there are
as many visuals in school textbooks, though the relative frequencies of the different
representations used differs between scientific journals and textbooks.
In psychology, the use of visual representations in the sciences (engineering,
technology, etc.) tends to be theorized in terms of mental representations
(images). That is, no distinction is made between what scientists and science
students make available to each other in face-to-face interactions or in written
media (journals, books, Web pages, chalkboards, read-outs from instruments) and
the unobservable processes of the mind. Thus, although the professor in Figure 1
only can be seen and heard articulating words (“and when you, when you
a:diabATically dEmagnetize it …”), producing gestures, employing intonation
and other speech variations, and moving and orienting his body, psychologists try
to study thought patterns that are not directly observable. But the students in the
physics course do learn how to use the diagram and how to communicate with it
from this lecture; and they do so without knowing psychology.
A very different approach has been proposed in the concept of inscription
(Latour, 1987). This term refers to every external representation other than text
used in the sciences, including graphs, photographs, micrographs, X-ray images,
diagrams, drawings, maps, equations, tables, and so on. Because they are external,
cultural-historical forms, they can be studied anthropologically. Rather than
concerning themselves with unobservable mental structures, anthropologists do
not attribute any special quality to the minds of inscription users but simply follow
how inscriptions are gathered, used, transformed, combined, and sent around in
the scientific culture and how these inscription-related processes change over
different time scales (including historical). It is only when there remains
something unexplained that we seek recourse to unobservable cognitive structures.
Thus, rather than trying to get into the mind the professor (Figure 1), we study
what he makes available to his students, which is precisely what to think and how
to think with the diagram he is presenting to them in the lecture. His gestures
already prefigure where there will be a horizontal line in the diagram, and they
articulate thought as it unfolds and before it is fixed on the board. The gesture is
part of cognition and it is from it that the students learn to think about graphs.
Research on inscriptions has shown the tremendous social-interactive work that is
involved in the use of inscriptions and their organizational (coordinating,
mediating, integrating) effects in scientific use, the coordination of people, and in
the learning of science.
The anthropological approach is consistent with the work of the Russian
developmental psychologist Lev Vygotsky (1997), whose work is widely used in
the educational sciences and whose work directs us how to think about cognition
and development. Two core issues from his theory about the functioning of mind
are important to the study of knowing and learning the use of inscriptions. First,
any higher-order cognitive function is the result of social interactions. That is, the
students watching and listening to the professor (Figure 1) learn how to use and
talk about the graph not by going into his mind or by trying to guess what is going
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on inside, but by carefully attending to what the professor makes public in and
through his communication. They further learn to use inscriptions while doing
assignments, a form of communication with the professor, or by communicating with
others over and about inscriptions. Second, Vygotsky suggests that speaking and
thinking are two separate but mutually influencing processes that themselves are
always developing. Speaking and thinking are combined into a superordinate
process that I think of as communication. Thus, the professor in Figure 1 does not
engage in a memory dump, an externalization of mental structure, but his speech
develops together with his thought, both subordinated to the communicative
process. This process also includes all the other communicative modes that we
may witness in the lecture, each being a one-sided expression of communication
and therefore thought. In this way, we can actually understand how and why this
professor incorrectly presented the topic of cooling by adiabatic demagnetization
in two consecutive lectures, only to catch his error later, while looking at a series of
equations and then correcting himself in a third attempt at presenting the curves
in Figure 1.
Participation and Identity
A third element of the situative learning perspective at a more macro level than activities and interactions are the practices they make up. Meaningful participation in practices is a central concept within the situative perspective especially in the work of Lave
and Wenger (1991) (Wenger, 1999) on situated learning and legitimate peripheral participation and ideas that preceded those such as cognitive apprenticeship (Brown,
Collins & Duguid, 1989). According to this concept, all actions and activities are guided
towards a larger goal and learning is about understanding that larger goal and aligning
actions with that. We are all embedded in different communities of practice as graduate
students, faculty, or as parents, and learning to participate in those communities is central to situated learning. Whatever we need to do to make sense and participate meaningfully is learning. The idea of a community of practice, and legitimate peripheral
participation in that community, underscores the highly contextual nature of human
learning. Furthermore, as we learn to participate we undergo an identity transformation
(Holland, Lachicotte, Skinner, & Cain, 1998; Lave & Wenger, 1991). The identities we
develop or reflect play a significant role in our learning trajectory (Eckert, 1989). In
Commentary 3, Indigo Esmonde of the University of Toronto discusses issues related to
identity in engineering. Rogoff (1990, 1995, 2003) studied children-parent and childcare interactions to examine learning of social skills. Guided participation and communities of learners ideas came from there. This work is also finding support from recent
work in neuroscience (Meltzoff, Kuhl, Movellan, & Sejnowski, 2009). Scientists are realizing that social aspects play a critical role although delineating which ones is not an
easy task. Vygotsky’s influence is also central to work by cultural psychologists and
particularly activity theory.
Commentary 3: Becoming an Engineer: Identity, Equity and Learning, Indigo
Esmonde University of Toronto
From a sociocultural perspective, learning is conceptualized as a process of
becoming a particular type of person. During the process of learning, people
develop what have been called practice-linked identities: “identities that people
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come to take on, construct, and embrace that are linked to participation in
particular social and cultural practices” (Nasir & Hand, 2008, p. 147). As any
individual participates in a variety of practices, they develop several practicelinked identities, and through their participation, they also develop repertoires of
practice, ways of engaging in activities that are related to their past forms of
participation (Gutierrez & Rogoff, 2003). Prospective and practicing engineers
develop repertoires and practice-linked identities associated with multiple
contexts of learning, including K-12 or university courses, workplace education
contexts, and any daily working contexts in which learning routinely takes place.
While these varied identities are not unrelated, each practice-linked identity is
integral to the context in which it emerges.
In analyzing opportunities to learn in engineering education, learning
contexts should be interrogated to discover the ways in which these contexts
allow participants to develop engineering-related identities. This is an equity
issue if identity development is supported for some participants, and not others.
Key questions include: how does the structure of the (engineering) practice
influence access to knowledge about engineering, practices of engineering, and
engineering identities? Similarly, how do interpersonal interactions within
these spaces influence access to knowledge, practices, and identities?
The structure of the practice is key to understanding whether
participants have equitable access to participation. For example, Nasir and
Hand (2008) investigated opportunities to learn for students in high school
basketball teams and mathematics classes. They argued that the teams offered
more opportunities for students to engage in the practice of basketball, more
opportunities for self expression, and offered each student the chance to take on
an integral role in the team. Professional and educational contexts in which
engineering practices are learned can be similarly analyzed. Interpersonal
interactions should also be considered. In a given context, one’s own identity
influences one’s opportunities to engage with the practice, but so do the
identities of others. In a study of a high school mathematics classroom, I
demonstrated that the presence or absence of a student considered ‘expert’ in
any cooperative group influences the opportunities to learn for all other group
members (Esmonde, 2009).
Although so far I have been describing issues of identity and equity as if
they are solely related to the practice, it is critical to consider how people’s preexisting identities, especially those related to broad social categories such as race,
class, gender, and so on, influence the nature of their practice-linked identities
(through, for example, influencing opportunities for participation). These social
identities may interact with practice-linked identities in overt ways, such as
programs designed to encourage women’s participation in science and
engineering fields, or more subtle ways. As Gutierrez and Rogoff (2003) point
out, “people do not just choose to move in and out of different practices, taking
on new and equal participation in cultural communities” (p. 21).
For the field of engineering education, then, the challenge is two-fold. It
is imperative to consider how contexts of learning can support the development
of positive engineering identities. This cannot be done without considering the
repertoires of practice that different social groups bring to these contexts.
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The Situative Perspective and Engineering Learning
We believe that the situative perspective offers many useful avenues for research on
engineering learning given three distinguishing characteristics of engineering learning:
use of representations, alignment with professional practices, and the emphasis on design.
The first element of engineering which is central to engineering learning and practice is
the use of representations. Like many other practitioners, engineers are surrounded by
tools and the purpose of many of these tools is to lead to representations that can help
guide the work of engineers. Graphs, charts, visuals, are all examples of representations
that engineers use on a regular basis. As a matter of fact, scholars have suggested that engineering can be seen as a discipline that teaches students how to convert one form of representation into another (McCracken & Newstetter, 2001). For instance, many problems
that engineering students work on, and that a practitioner faces, are expressed as text that
needs to be converted into another symbol system, often visual. A free body diagram is a
prime example of such a conversion (also see Roth’s Commentary 2 on inscriptions). Increasingly, the role of producing representations is being played by digital technology
leading to an era of production and exchange of representations which is unprecedented
in human history. The use of tools is also leading to collaboration among engineers, aided
by representations, that is swiftly but decisively reinventing engineering cognition (Pea,
1985) and practice, akin to the change brought about by the first wave of information
technology use in manufacturing (Zuboff, 1989) and now being fostered by digital environments and large scale cyberinfrastructure (see Commentary 4 by Madhavan). The role
of technological tools, particularly digital tools, is extremely under-theorized in engineering education and a perspective of representational mediation can prove useful to develop
a deeper understanding of technology use and design. The potential exists not only for
changing how we teach and learn, but our research practices themselves.
Commentary 4: The Role of Cyberinfrastructure in Engineering Education, Krishna
P.C. Madhavan, Purdue University
The growth of the Internet as the fabric for exchanging information ranging from commercial services to scientific insights has fundamentally changed our
daily lives. High-speed networks enable connecting together tools for data collection, data aggregation, scientific analyses, and visualization. More importantly,
these technologies, also known as cyberinfrastructure, are powerful at connecting
people in unprecedented ways leading to community formation. Large-scale engineering cyber-environments such as nanoHUB.org, the George E. Brown Jr.
Network for Earthquake Engineering, the Earth System Grid, and the Cancer
Biomedical Informatics Grid are effectively blurring the line between engineering
practice, research, and learning.
Cyberinfrastructure serves two roles in the context of engineering education.
In its more well-known role, cyberinfrastructure serves to deliver cutting-edge
content to a large community of learners. In this role, cyberinfrastructure enables
cyberlearning defined as “learning that is mediated by networked computing and
communications technology” (NSFTFC, 2008). In its second role, cyberinfrastructure plays the role of enabling fundamental research within the field of engineering education.
In its first role, cyberinfrastructure facilitates the delivery of information in
novel forms. Many learning environments routinely incorporate online lectures,
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interactive animations, podcasts, and game-based paradigms to name a few flavors
of cyberinfrastructure use. Even environments such as YouTubeTM and iTunes
UTM may be viewed as cyberlearning platforms. If theory, experimentation, and
modeling & simulations mark three primary pillars of engineering, cyberinfrastructure can play a critical role in enabling students to understand the variety of
approaches available through each. For example, projects such as the iLab Network
allow students real-time access to remote instruments through a cyber-environment.
iLab allows students to perform experiments, build on existing knowledge, predict
results, and explore. Similarly, cyber-environments such as nanoHUB, provide
learners with access to highly sophisticated computational resources transparently.
This allows students to explore and model new problem spaces that were not previously possible. While a non-trivial technical endeavor, one very viable approach that
is currently being explored is to link such remote instruments with modeling and
simulation environments thus enabling students to understand the interconnected
nature of engineering problem solving.
The National Academy of Engineering (NAE) identified the grand
challenge of “advancing personalized learning” (NAE, 2008). Cyberenvironments, by their innate ability to be instrumented fully, provide us with an
opportunity to personalize learning environments. For example, nanoHUB is
beginning to explore the notion of user flow informatics, which attempts to
understand patterns of tool and resource usage within the HUB environment.
Software available within the cyber-environment acts as a sensor in identifying the
preferences and bottlenecks faced by each learner. This deep insight, when fed
back into the cyber-environment, allows educators to receive real-time insights
into student performance and provides appropriate learning pathways. Indeed,
they help in understanding the efficacy of the learning material also. In
combination with currently available research on intelligent tutors and growing
knowledge of how people learn, cyber-environments have real promise for
delivering on the dream of personalized learning.
The second role of cyberinfrastructure in acting as a platform for facilitating
research is a more challenging issue. The National Science Foundation (NSF)
Atkins Report (Atkins, 2003) outlines the need for a cyberinfrastructure. In the
field of engineering education research both within and outside the U.S.,
cyberinfrastructure is just beginning to gain traction as a platform for facilitating
research.
The emergence of engineering education as a problem space of rigorous
research puts into sharp relief the need for educational researchers to utilize tools
that other engineering disciplines use on a day-to-day basis. Knowledge production
in the field of engineering education is vibrant, highly distributed, and fragmented.
It has every characteristic of a complex virtual community. Much progress needs to
be made in fully leveraging cyberinfrastructure to accelerate the transition of data to
knowledge in engineering education, and many efforts that are currently underway.
For example, research leveraging large-scale data in combination with well
established visual analytic methods in the field of social network analysis is
beginning to provide new insights into the state of knowledge within engineering
education research (Madhavan et al., 2010). While this application shows the
power of cyberinfrastructure in studying the emergence of engineering education
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research, it is just one of the many facets of cyberinfrastructure use in research. The
holy grail of cyberinfrastructure use continues to be the development of
environments that transparently transition between engineering practice, research,
and education.
A second critical aspect of engineering and engineering learning is its close association
with professional practice. A majority of engineers pursue the profession to be able to work
as engineers. Therefore, an inherently large aspect of their training is learning to become a
part of the community of practice of professional engineers. This includes developing an
identity as an engineer in numerous ways and forms. Professional practice is also collaborative in nature and therefore learning to work as part of groups and teams is essential to
engineering learning. Of course, work settings have played a crucial role in informing
early work in the field (Scribner, 1997a, 1997b; Wenger, 1998) and this is one aspect of
engineering learning which we believe has been studied extensively by scholars in aligned
disciplines such as technical communication (Winsor, 1996), science education (Lemke,
1997), technology studies (Bucciarelli, 1994), architecture (Schon, 1983), engineering
studies (Johri, 2010a), and also learning scientists (Hall & Stevens, 1995; Stevens, 2000)9.
Yet, the disjuncture between school-engineering and work-engineering remains intact
and significant efforts are needed to bridge this gap. Engineers in the workplace often say
that even technical skills are easier to learn on the job as compared to formal training.
They often complain that very little of what they learn in school is of any use to them.
There is an issue of situated learning and transfer. Recent work has started to capture this
tension (Stevens, O’Connor, Garrison, Jocuns, & Amos, 2008). There has to be effort
that links research with practice on an ongoing basis as the environment for work and
learning changes rapidly.
A final element of engineering learning that is unique compared to mathematics and science learning is design. Engineers are by definition designers. Engineering design thinking
and learning are central to the development of an engineer (Dym, Agogino, Eris, Frey &
Leifer, 2005). Yet, design has its own unique ways of developing cognitive and situated skill
requirements. It requires skills with materials, ability to work collaboratively, and the ability
to become part of a community of practice. Models of teaching design therefore differ from
teaching engineering science-based content to students. Design is also a useful metaphor to
think about engineering learning research that can lead to innovations. Design-based research (a descendent of work on design experiments) is a useful paradigm that can be adopted by engineering learning researchers. It also highlights that ideas from core engineering
disciplines can be used to improve engineering learning if applied with the right understanding of context. Iris Tabak of Ben Gurion University of the Negev discusses design research in Commentary 5.
Commentary 5: Design-based Research Methods and Engineering Education, Iris
Tabak, Ben Gurion University of the Negev
Engineering education faces unprecedented challenges. Engineering
educators are called upon to help learners develop analytic, communication, and
9
See Lemke (1997) for more comprehensive discussion of the disjuncture between school practices and
professional practices and the effort that must be made to integrate students’ school and professional trajectories. The discussion is highly applicable to engineering learning given the professional and applied nature
of our practices.
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teamwork skills, while meeting ever increasing content demands and cultivating
independent learners. Although innovative teaching methods with documented
success exist, there is still much that we do not know about how to achieve these
goals. Thus, it is necessary to simultaneously develop novel approaches to
learning and instruction, examine how they can be adopted, and ascertain
whether and how they are achieving their intended aims (Streveler & Smith,
2006). Design-based research methods (DBRM) offer a productive means to
accomplish this.
Roughly, design-based research methods are an iterative cycle of design,
enactment, and empirical study of an innovation in a naturalistic setting (Design
Based Research Collective, 2003). DBRM are especially useful when there is
dissatisfaction with extant circumstances, but desirable alternatives exist only
as a vision. Thus, it is necessary to design an innovation as a tangible first
approximation of this vision.
DBRM incorporates pedagogical and curricular design knowledge, as well
as cognitive and sociocultural learning theories in the design of this innovation.
The design attends to varied aspects: activities, curricular sequences, teacher
guidance, classroom social organization, and cohesion with local practices.
This broad conceptualization of learning environments is key to successful
implementation in non-research settings. This design is then enacted in actual
classrooms.
Once the design is enacted, concrete tangibility replaces ethereal ideation
and the approach can now be scrutinized. DBRM draws on both quantitative
and qualitative measures to identify whether and how learning occurs.
Emphasis is placed on deriving process models that describe how different
facets of the design contribute to microlongitudinal changes in knowledge and
practice of both learners and teachers. For example, pre/post tests might be
used to measure changes in conceptual understanding, and open-ended
observations might be used to document how a particular conception is
articulated in successively better ways through interactions between peers,
materials, and teachers.
Designs that operate in real-world settings are complex, multifaceted, and
interact with existing contextual factors. In some cases, making a design work in a
particular setting necessitates changes to the design. This is why DBRM involves
an iterative process within as well as between enactments. Empirical study enables
designers to understand the strengths and weaknesses of their original designs, and
to incorporate efficacious elements that emerged through an enactment as
purposeful elements of subsequent redesign (Tabak, 2004). As the design is
refined and evidence in support of the design accrues, an effort is made to
articulate the design at both a principled and a concrete level in order to assist in
dissemination. The empirical aspect of DBRM also produces new insights on the
nature of learning particular topics and skills.
Design-based research methods can offer a way for engineering educators
to achieve the fields’ ambitious goals in an evidence-based way that advances both
theory and practice (e.g., Carstensen & Bernhard, 2007). DBRM, like all
methods, needs to develop and change. In particular, future endeavors should
consider how to better integrate large-scale quantitative measures with process
measures, and how to obtain process data in a more cost-effective way.
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Corresponding to the three characteristics of engineering we have outlined, we believe
that the three elements of situated learning we have identified can make a significant contribution to furthering our understanding of engineering learning in unique ways, similar
to its role in biomedical engineering education (Harris, Bransford, & Brophy, 2002).
These connections have already started to manifest themselves in recent articles in engineering education publications. For instance, Paretti (2009) uses the situated learning
approach, in conjunction with activity theory, to examine communication practices in a
capstone design class. Pierrakos, Beam, Constantz, Johri, & Anderson (2009) use emerging literature on situated identities to investigate the different pathways and experiences
of students who persist with engineering versus those who switch out of engineering.
Similar examination of identity has also been done by Stevens et al. (2008). Gill, Sharp,
Mills, and Franzway (2008) build on anthropological investigations of the workplace and
the communities of practice literature to draw attention to gender issues in professional
engineering settings. They find that the positive self image women had in school was not
maintained in the workplace given a lack of women role models in immediately higher up
positions in the office hierarchy. In an upcoming article Johri (in press) introduces the
concept of “sociomaterial bricolage” to capture the essence of engineering work practices
on global teams. Software engineers in his study make do with whatever resources are
available to them to develop work practices that span geographic dispersion, this ability to
“make do,” he argues, is the essence of most engineering work. In relation to digital technology, Johri and Lohani (2008) draw on guided participation and communities of
practice frameworks to examine the role of representations in large engineering classes.
They argue that a content-transfer model of technology use undercuts the benefits that
are available with digital technology, particularly pen-based technology, to engage
students in mutual construction of engineering representations. These studies illustrate
welcome progress but we believe that enormous potential still exists to make significant
theoretical contributions (Johri, 2010) and with that aim in mind we have outlined some
potential research ideas in Table 2.
CONVERGENCE AND GROWTH: A SCIENCE OF ENGINEERING LEARNING AND BEYOND
As we suggest earlier, there is significant potential for engineering learning research to build on research on learning undertaken by the learning science community.
We show how one theoretical idea, situativity, can shape research on engineering
learning. As the field of engineering education research has matured, more alliances
are being forged between that community and the community of the learning
sciences, which, as discussed above, has traditionally focused more on K-12 issues.
This is evidenced in a number of ways including the inclusion of learning scientists
on the faculty of engineering education programs at both Virginia Tech and Purdue;
the acceptance of a workshop on engineering education and the learning sciences for
the summer 2010 meeting of the International Society for the Learning Sciences; and
increasing collaborations between engineering researchers and learning scientists.
We believe that knowledge does not have to flow in just one direction and that the
fields can benefit each other mutually. For engineering learning scholars, one of the
measures of success in the future will be the ways in which they shape other fields and
disciplines such as learning sciences (Shulman, 2005). There is significant development, for instance, on cyberlearning in engineering education which has the potential
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TABLE 2
Elements of Situativity and Implications for Engineering Learning Research
to inform the development and deployment of technology for learning in other fields
(see Commentary 5 by Madhavan).
This article focuses primarily on the situative perspective on learning as we believe that
in the short and medium terms, it offers the best body of knowledge and alignment with
engineering education to build a science of engineering learning. Yet, we recognize that it
is only one perspective and many other viable options exist. For instance, recent scholarship suggests that future educational research and practice is likely to be shaped significantly by recent developments in brain sciences and neuroscience (Bransford et al; 2006;
also see Commentary 6 by Schwartz and Tsang). Interestingly, initial work in the area
demonstrates what has been the prevalent idea in situative theory that, “human children
readily learn through social interactions with other people” (Meltzoff et al., 2009, p. 285).
The social basis for cognition once again points out the tough road ahead in reconciling the
social and the individual in learning. Furthermore, as Commentary 6 shows, for any new
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field that draws on significantly new instruments and methods there is a large gap that
needs to be overcome between deterministic adoption of a method and its often situated
and long term effect on actual scientific practices. The approach taken by Bransford et al.
(2006), and the Learning in Formal and Information Environments (LIFE) Science of
Learning Center, is one way of overcoming this gap (also see their chapter in the
Cambridge Handbook of the Learning Sciences). The scholars engaged with that center argue
that the next decade of learning sciences will see a convergence between informal learning,
formal learning, and implicit learning and the brain. This synergistic integration will
follow the major changes that learning research has undergone from drawing on findings
from studies conducted only in the laboratory to studies conducted in complex setting such
as a classroom (p. 210).
Commentary 6: How Could Neuroscience Have Practical Applications for Engineering
Education?, Dan Schwartz and Jessica Tsang, Stanford University
Cognitive neuroscience is a small branch of neuroscience that examines the
relations between intelligent behavior and brain functioning. Educators often
wonder if cognitive neuroscience will provide practical benefits for improving
instruction. We briefly review three possibilities: direct instructional applications,
clinical treatments, and theoretical re-orientations.
There is substantial interest in direct translations of neuroscience to the
classroom, or brain-based education. Succeeding will require better bridges
between the basic science of brain research and the applied aims of educational
research. Physics and engineering have had centuries to build their bridges. In
neuroscience, a study might demonstrate a brain region is necessary for
mathematics by disrupting its function and noting a decrement in numerical
processing. Educators, however, would be more interested in whether
strengthening this region improves numerical processing. Another brain study
might inch closer to educational goals by showing that basic rewards stimulate
plasticity in the target region. However, educators would ask whether rewards
negatively impact students’ interest in mathematics, for example, by undermining
their intrinsic motivation. We need to build win-win collaborations where the
search for necessary mechanisms and the design of sufficient conditions for
learning can complement one another.
In the context of clinical populations, excitement over practical applications
has a stronger basis, where the need to improve a specific dysfunction legitimates
unusual treatments. Working with clinical populations is a strong suit for
neuroscience, understanding a dysfunction advances the science while successful
remediation provides important causal evidence. Although tangential to most
engineering students, clinical approaches will be important for helping younger
students who might otherwise never make it to an engineering program. For
example, by measuring electrical brain activity, one can identify infants at risk
for dyslexia before language has developed. Thus, remediation can start before the
child has already started to fall behind. (See Gabrieli (2009) for a synthesis of the
literature.)
Most immediately, we believe neuroscience can spark theoretical reorientations. There is nothing as practical as a good theory. We are not
recommending that engineering educators study neurotransmitters to become better
teachers. Rather, neuroscience provides broad models and generative analogies that
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can influence how people think about learning. One example is the finding that
there are brain networks involved in any school task. For example, we have
discovered that determining the midpoint between -3 and 3, for example, activates a
different network from deciding which digit is greater. Networks displace the
common folk model that learning is like strengthening a muscle, and assessment is a
test of mental strength. Intellectual growth is more like learning to dance, and a goal
of instruction is to help students coordinate the different evolutionary functions
of the brain into a new culturally mediated dance. The simple switch from learningas-strengthening to learning-as-coordinating has generative possibilities for the
design of instruction, and is an example of the broad theoretical shifts that
neuroscience will precipitate in the behavioral sciences.
To accelerate educationally relevant theoretical contributions, we have
two recommendations. (1) In addition to examining the biological basis of
individual brain differences, research should focus on how context and
experience affect brain function and behavior. This is the piece of learning that
educators can influence, and such a shift in focus would welcome social and
cultural theories that emphasize contextual factors. (2) Research should choose
problems relevant to enduring questions in education, for example, “What is the
value of hands-on activities?” or “What are the unique consequences of discovery
versus being told?”
Neuroscience will influence education through theory, remediation for
clinical cases, and eventually, practical applications for everyday students. The
most high-impact work will develop when researchers from different traditions
distance themselves from their “routine expertise” to create a new but shared set
of goals.
CONCLUSION
The age that we live in has variously been characterized as the information age,
the knowledge economy, the network society, and/or the global era. Irrespective of
the nomenclature, the core idea that remains constant is that we live in a rapidly
changing environment where our ability to learn fast and reinvent ourselves is key for
survival and growth (Bransford, 2007). Furthermore, complexity of human social and
engineered life has never been higher. This significantly affects our ability to make
sense of the world around us and to act intelligently. This change is reflected in the
professional life of engineers where they have to work with novel technologies, with a
diversity of people around the world, as part of highly interdisciplinary teams, and on
projects that are complex both in scale and expertise. What engineers need is adaptive
expertise which allows them to be both innovative and efficient at what they do.
From an educator’s viewpoint, this is the world we inherit and need to prepare our
students to enter. Therefore, it is imperative that we reflect on the skill development
we need to facilitate and design implementable learning environments for our
students. This forms the core agenda for the emerging discipline of engineering
education (Haghigi, 2005).
In this paper, we have laid out a broad theoretical agenda to serve as a toolkit for understanding and designing learning. Throughout the paper we have introduced several
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scholars and their ideas with the intention to spark an interest in readers so that they will be
motivated to learn more. We hope that readers will engage more with the ideas of situative
learning, lifelong learning, informal learning, and personalized learning (one of the key
challenges identified in NAE’s grand challenges for engineering). Almost three decades
ago Becker (1972) argued, somewhat controversially, that a school is a lousy place to learn
anything and we need to rethink and retool our conception and implementation of schools.
We now have the tools and technology and need to engage a broad audience to bring about
an innovation in how we organize our resources to make engineering learning and education available to a larger number of students and help them to engage in it successfully.
Lemke (1997) captures the problem faced by engineering education scholars quite aptly
when he states that: “Schools are communities of practice that do not preach what they
practice. They teach about practices of other communities, for example, about the practices
of science and mathematics, but say very little about the practices of schooling” (Lemke,
1997; p. 52-53). It is time we made an effort to investigate our practices closely with the
aim to innovate.
ACKNOWLEDGMENTS
We are grateful to Jack Lohmann and the editorial team for this special issue, Caroline
Baillie, Edmond Ko, Wendy Newstetter, and David Radcliffe, for providing us with the
opportunity to contribute this article and for their guidance and suggestions throughout
the editorial process. We particularly wish to acknowledge the assistance and expertise provided by Wendy Newstetter during the writing and revising of the manuscript and for
shepherding us through this process efficiently, and we hope, effectively. We also wish to
express our gratitude towards the nine reviewers for providing superb and actionable feedback and suggestions. Jack Lohmann provided excellent, and detailed, comments on the
manuscript that greatly improved its readability and Donna Riley further ensured that all
loose ends were tightened. Any remaining errors are the authors’ responsibility. Finally, we
want to thank the expert contributors for agreeing to add their voice to this manuscript and
for doing so in a timely manner.
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AUTHORS
Aditya Johri is assistant professor of Engineering Education and director of toolsLAB
(Technology, Open Organizing, and Learning Sciences laboratory) at Virginia Tech,
Blacksburg, VA 24061; ajohri@vt.edu.
Barbara M. Olds is senior advisor to the Directorate for Education and Human
Resources (EHR) of the U. S. National Science Foundation, 4201 Wilson Blvd.,
Arlington, VA 22230; bolds@nsf.gov.
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