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Analyzing Cross-disciplinary Design Teams

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Abstract

Seventy student teams in a university service learning program were studied to assess the nature of cross-disciplinary team collaboration, learning, and performance. The development of assessment tools was grounded in three theories/frameworks: activity theory, cross-disciplinary learning, and performance support systems. Using a comparative, multiple-case study design in this mixed methods study, questionnaires were administered to all teams, while observations and interviews were conducted with select teams. The relationships and contradictions within team activity systems, barriers and enhancers to performance, and the degree to which teams evolved toward cross-disciplinary learning during projects were examined. Results indicated that students on CDL teams worked with teammates from different disciplines; made decisions through consensus; and appreciated others techniques and approaches with more frequency than non-CDL teams. The use of activity theory, and cross-disciplinary learning and performance support frameworks to assess teams as a foundation for the design of Web-based collaborative learning environments were discussed
Session T1H
1-4244-0257-3/06/$20.00 © 2006 IEEE October 28 – 31, 2006, San Diego, CA
36th ASEE/IEEE Frontiers in Education Conference
T1H-17
Analyzing Cross-disciplinary Design Teams
Scott P. Schaffer1, Kimfong Lei 2, Lisette Reyes 3, William Oakes 4, and Carla Zoltowski5
1 Scott P. Schaffer, Educational Technology, Purdue University, ss@purdue.edu
2 Kimfong Lei, Educational Technology, Purdue University, kimfong@purdue.edu
3 Lisette Reyes, Educational Technology, Purdue University, lreyespa@purdue.edu
4 William Oakes, Engineering Education, Purdue University, oakes@purdue.edu
5 Carla Zoltowski, Engineering Education, Purdue University, cbz@purdue.edu
AbstractSeventy student teams in a university service
learning program were studied to assess the nature of
cross-disciplinary team collaboration, learning, and
performance. The development of assessment tools was
grounded in three theories/frameworks: activity theory,
cross-disciplinary learning, and performance support
systems. Using a comparative, multiple-case study design
in this mixed methods study, questionnaires were
administered to all teams, while observations and
interviews were conducted with select teams. The
relationships and contradictions within team activity
systems, barriers and enhancers to performance, and the
degree to which teams evolved toward cross-disciplinary
learning during projects were examined. Results indicated
that students on CDL teams worked with teammates from
different disciplines; made decisions through consensus;
and appreciated others techniques and approaches with
more frequency than non-CDL teams. The use of activity
theory, and cross-disciplinary learning and performance
support frameworks to assess teams as a foundation for
the design of web-based collaborative learning
environments were discussed.
Index TermsActivity theory, Cross-disciplinary learning,
Performance support, Team collaboration, Service-learning,
Engineering Projects in Community Service, EPICS
INTRODUCTION
Study of peer interaction and discussion in face-to-face
settings [12] and in computer supported collaborative learning
environments [13] has suggested the need for specific kinds of
support to facilitate interaction regardless of setting. More
recently the design of online and blended learning
environments based on social-cultural activities has begun to
attract the attention of researchers [1]. Furthermore, there is
increasing recognition of the importance and contribution of
cross-disciplinary teams to an organization’s competitive
advantage [3], [8], [11]. Thus, it is increasingly important to
understand how to increase the effectiveness of teams
engaging in collaborative online communities. The design of a
support system to support such teams as they engage one
another during projects is critical to development of a
sustainable learning and knowledge sharing environment.
A framework was developed to explore the nature of the
learning environment and other socio-cultural factors for
several teams in a university setting. In addition, a cross-
disciplinary team problem solving model was developed based
on review of the literature and previous studies by the
researchers. The framework elements and the model processes
are described in the next section.
Our contributions are two-fold: introduce a framework
and methods to assess cross-disciplinary team learning and
identify performance support system enhancers and barriers
with emphasis on systemic and socio-cultural perspectives.
CROSS-DISCIPLINARY TEAM LEARNING (CDTL)
FRAMEWORK AND MODEL
A broad framework and corresponding process model was
developed to study cross-disciplinary teams as they attempted
to solve design problems. The framework and model are based
primarily on the prior work of these researchers and the
literature on team problem solving. The three elements of the
framework are: activity systems, cross-disciplinary learning,
and performance support elements.
Activity Systems
Activity systems are derived from Activity Theory which is a
multi-disciplinary theoretical framework for studying human
activity from a cultural-historical perspective. Activity theory
has roots in the works of Vygotsky [19] and his colleague
Leont’ev [10] in 1920s and 1930s. Engestrom [4] further
modified the theory and included several socio-cultural system
elements. The theory provides an analytical framework that
stresses the importance of a cultural-historical and dialectical
approach to the understanding of human activity and societal
or systemic change. Activity theory is utilized to understand
the structure of collaboration within a systems view, including
the object, outcome, subject, tools, community, rule, and
division of labor within a particular activity system [1], [9], as
illustrated in Figure 2.
Cross-Disciplinary Learning
For university students in professional programs such as
engineering, or any number of design fields, e.g., software,
graphics, interior, instructional, at the university level, cross-
disciplinary learning experiences are important to their future
success in a global marketplace [6]. Sidhu, et al. [18] found
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36th ASEE/IEEE Frontiers in Education Conference
T1H-18
that many problems engineers experienced were related to
globalization and were cross-disciplinary in nature. Such skills
as understanding more than one discipline and the ability to
communicate with people with different skill backgrounds
were cited as necessary in any program preparing professional
engineers. In the same study, another interdisciplinary
problem was identified between engineering sub-disciplines
(electrical, mechanical, computer science). Other issues
industry executives identified were related to business and
legal issues related to the production and marketing of
products.
In theory, cross-disciplinary learning (CDL) and
knowledge-sharing promotes innovation and cross-fertilization
of ideas. The willingness and ability to understand and
integrate the language, models, frameworks and rationales of
team members from other disciplines frees them to think more
deeply than teams that do not attempt to think in a cross-
disciplinary manner [7]. In assessing CDL within
architecture, construction, and engineering undergraduate
students, Fruchter and Emery [7] identified four aspects of
team evolution throughout projects: 1) individual expertise
development, 2) awareness of other disciplines, 3)
appreciation of their techniques and approaches, and 4)
understanding of these techniques and models to the degree
that conversations about design incorporated the language of
all team members.
Performance Support Systems
Finally, a performance support systems framework [15], [14],
[15], [20], as shown in Figure 1, has been found to be an
effective way to optimize performance in technology
enhanced learning environments. Inputs into a system such as
resources are aligned with vision and objectives in an effort to
support effective performance. Performance support includes
administrative supports such as: expectations, feedback,
rewards, incentives, tools, and processes, as well as individual
supports such as: motivation, capacity, and skill and
knowledge development. Missing elements of the performance
support system within a project team will increase the
likelihood of poor performance or ineffective outcomes.
Cross-Disciplinary Team Learning
We propose using the following model in conjunction with the
previously described framework to begin to understand how a
collection of individuals becomes a team.
The cross-disciplinary team learning (CDTL) model has
three major elements:
1. Identification
2. Formation
3. Adaptation
Identification is primarily an individual process that
involves self-assessment to determine the degree to which one
feels like they can contribute to the team. This process is
psychological in the sense that self-efficacy beliefs and other
motivational factors are involved at this stage of a project, and
it is cognitive in the sense that a high degree of information
seeking and finding are occurring. This process requires a
great deal of assimilation and knowledge integration on the
part of individual team members that continues throughout the
project.
FIGURE 1
PERFORMANCE PYRAMID (RETRIEVED FROM
HTTP://WWW.COE.MISSOURI.EDU/~PYRAMID/).
Formation is a linking process that involves many of the
core structures and processes of team functioning. As
individuals attempt to identify with team visions and goals, the
team is being formed structurally. Team roles are identified
and a team leader emerges. Meetings begin to happen and the
initial interactions between team members focus on aligning
strengths and needs relative to achieving goals. Initial self and
team assessments of the fit of each individual skills and
experiences with project demands triggers cross-disciplinary
learning. Team members become aware of others expertise
areas to some degree and may be motivated to learn more
about those areas.
Adaptation is an integrating process. The team has formed
relationships and structures and processes for its functioning
are in place. Many of the elements of the formation process
are continually being assessed and revised. The degree to
which the team feels confident in the quality of its formation
impacts its collective efficacy. Thus, the self-efficacy of
individuals is impacted by linking processes during formation
which in turn affect collective or team efficacy as the team
adapts. In effect, the quality of the early team member
experiences has a great impact on the team capacity for
growth. Teams that are cross-disciplinary will engage in
transformational processes at this point in their evolution.
They will learn about one another’s expertise area and will
discuss the project from different perspectives. These teams
will adapt their design processes to accommodate these
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36th ASEE/IEEE Frontiers in Education Conference
T1H-19
perspectives. Products and processes of such teams reflect this
synthesis of ideas and are exemplary, i.e., innovative, creative.
The above frameworks were the foundation of a study of
teams in a service learning program, Engineering Projects In
Community Service (EPICS), in a Midwestern university [2].
The purpose of this study was threefold: 1) identify the
elements and the relationships and contradictions in the
activity systems; 2) examine the barriers and enhancers to
team performance and satisfaction; and 3) assess CDL in
EPICS teams.
METHOD
A mixed-method approach was utilized to conduct this study
in Fall 2005. Questionnaires were administered to
approximately 250 students on 70 teams in the EPICS
program. Observations and interviews were conducted with
seven teams.
Data were collected at the individual and team levels.
Two types of questionnaires were administered to 250
individuals and 70 teams. An online questionnaire was
administrated to collect individuals’ responses to items
developed based on CDL and Performance Pyramid at the
middle of the semester. A paper-based questionnaire was
administrated to all project teams at the end of the semester.
The teams were asked to collaborate on a team response to the
items related to CDL and team efficacy, i.e. the team’s
confidence on the output of the project phrase.
A comparative, multiple case study design was used to
select seven teams from a large number of teams. The multiple
cases were analyzed for the purpose of theoretical replication,
which either (a) predicts similar results or (b) produces
contrasting results but for predictable reasons. The
development of a rich, theoretical framework is an important
step in all of these replication procedures [21]. Multiple cases
were compared with the elements of the Activity Theory
including: subject, object, outcome, tool, rules, community,
and division of labor. From the perspective of Activity
Theory, three principles for the interpretation and analysis of
the data were followed as recommended by Engestrom [4].
First, the activity system as a whole is the unit of analysis.
It is an important to note that short-term goal-directed actions,
as well as automatic operations, are relatively independent but
subordinate units of analysis, eventually understandable only
when interpreted against the background of entire activity
systems [5]. A communal motive which is embedded in the
objective or goal of the activity drives a collective object of
the activity. Second, the history of the activity system, e.g.
EPICS, is taken into account. Third, the tensions and
contradictions within the systems are analyzed as the source of
disruption, innovation, change, and development of the
system.
Purposeful sampling was used to select seven sub-teams
in this qualitative study. Different types of teams were studied
including the class levels of the students, the size, the gender,
and different disciplines. An observation protocol was
developed based on activity theory and CDL in addition to the
fieldwork approach. The observation protocol was revised
after expert review and feedback. Two researchers observed
selected teams in their meetings for 6 weeks. A parallel
structured interview protocol was also developed and
implemented for the interviews at the end of the semesters.
RESULTS
Questionnaire results indicated that some teams appeared to
evolve from being a collection of individuals to a collaborative
team that shared understanding. Barriers and enhancers of
team cross-disciplinary learning varied widely across teams
and but many teams were not clear as to how to work together
to solve problems. Specific results across the 70 teams that
responded to team questionnaires will be discussed in this
section.
Activity Systems
Data collected from observations and interviews with the
seven teams allowed testing of the activity system-based
protocols. The intent of such protocols is to describe the
system and system interactions.
Based on the data collected, a generic activity system for
a project in EPICS is illustrated in Figure 2. Elements of the
system are described below.
Subject
The subject is the individual or groups whose actions are the
focus of analysis. For the purpose of this paper, the students in
a sub-project team are the subject of the activity system.
Object
The collective object of the activity system is the completion
of a community project that addresses the needs of the
community partners with emphasis on creativity, innovations,
user ability, and etc.
Outcome
Outcomes include the gain or increase in: technical skills,
understanding of the design process, communication skills,
ability to work on a team, resourcefulness, organizational
skills, awareness of the community, awareness of the customer
in an engineering project, awareness of the customer in an
engineering project, and awareness of ethical issues [2].
Tools
A variety of tools are included. For example, information
tools, e.g. content management and knowledge management
systems; communication tools, e.g. online discussion forum,
EPICS website, listserv, email and etc.; technology tools, e.g.
hardware and software for analysis, design, and development;
as well as other tools that support problem-solving and project
management.
Community
The community, which extends beyond the university, is
comprised of project teams and sub-teams, EPICS classroom,
discipline/major area, teaching assistants, instructors, advisors,
contractors, and community clients.
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36th ASEE/IEEE Frontiers in Education Conference
T1H-20
FIGURE 2
ACTIVITY SYSTEM FOR A PROJECT IN EPICS
Division of Labor
The division of labor refers to both the horizontal division of
tasks between the members of the community, and to the
vertical division of power and status. Horizontal divisions of
tasks or roles include Team Leader, Project Leaders, Project
Partner Liaison, Lab Key Keeper, Second Lab Key Keeper,
Team Webmaster, EPICS Advisory Council, Financial
Officer, Manager of Intellectual Property, First Year Liaison,
and other roles for particular project tasks. The vertical
divisions of power and status include: students-students,
students-teaching assistants, students-advisors, students-
client, etc.
Rules
Rules refer to the explicit and implicit regulations, norms and
conventions that drive actions and interactions within the
activity system. In EPICS, these include EPICS Course
Policy, Engineering Design Process, Industrial Standards and
Ethics, and etc.
Performance Pyramid
Individuals’ responses to an online questionnaire (n = 213)
provided the findings to identify barriers and enhancers from
individuals’ perspective. They were asked to rate the
statements based on the Performance Pyramid. As shown in
Figure 3, the top three barriers to individuals’ performance are
feedback, expectations, and rewards and incentives. The
percentages of the neutral responses which were double digits
implied that students might have doubt about expectations,
feedback, and rewards and incentives while working on the
project team. The statements used in the questions were:
y I receive regular and helpful feedback about how
well I am meeting expectations regarding working
as a team member. (Feedback)
y I have received explicit expectations regarding
working on the team. (Expectation)
y There are rewards and incentives for me to work on
the team. (Rewards & Incentives)
Cross-Disciplinary Learning
Ratings on CDL were collected using an online questionnaire
in mid-semester. Results indicate that 40% of students rated
themselves as having an “Appreciation” of other disciplines,
and 29% rated as having an “Understanding” of other
disciplines.
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36th ASEE/IEEE Frontiers in Education Conference
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FIGURE 3
INDIVIDUALS RATING ON PERFORMANCE PYRAMIDS ITEMS (N = 213)
FIGURE 4
INDIVIDUALS RATINGS ON CROSS-DISCIPLINARY LEARNING (CDL) IN THE
MIDDLE OF THE SEMESTER (N=216)
Teams’ ratings on CDL were collected with a paper-
based questionnaire at the end of the semester. Each cross-
disciplinary team, which was a team with members from
different majors, was asked to discuss and reach the
consensus on a rating of a 10 to 100 scale. Figure 5 shows that
87.5% of the 40 teams rated themselves 70 and above. For a
rating of 70, the following statement anchored the students to
describe their experience working with their teammates as
follows: “We work together on most aspects of the project
and regularly share expertise and ideas with one another. Our
decisions are often made through consensus and with an
appreciation of one another’s disciplines. Our design reflects
cross-disciplinary collaboration and contribution.”
FIGURE 5
TEAMS RATINGS ON CROSS-DISCIPLINARY LEARNING (CDL) AT THE END OF
THE SEMESTER (N=40)
Both CDL teams (n=40) and Non-CDL teams (n=17)
were asked to rate their confidence on the quality of the
outputs in different project phases. As shown in Figure 6,
Non-CDL teams had lower ratings than CDL teams in all
different phases. For all teams, confidence ratings were lower
in the later phases including Detailed Design, Production, and
Service/Maintenance. The highest ratings for all teams were
of the Problem Identification phase and for the overall project.
FIGURE 6
COMPARISONS OF TEAMS RATINGS ON THE QUALITY OF PROJECT OUTPUTS
CDL TEAMS VS. NON-CDL TEAMS
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DISCUSSION AND CONCLUSION
The identification of the elements of the activity system in
EPICS helped us to consider the levels of contractions in the
activity system. The levels are as follows:
y Level 1: Primary inner contradiction within each
constituent component of the central activity.
y Level 2: Secondary contradictions between the
constituents of the central activity.
y Level 3: Tertiary contradiction between the object of the
dominant form of the central activity and the object of a
culturally more advanced form of the central activity.
y Level 4: Quaternary contradictions between the central
activity and its neighbor activities.
The impact of the following potential tensions and
contradictions keep the EPICS teams activity systems in a
state of instability.
Object: Decisions about product features (Level 1). Instructor
or advisor contributions to learning how to problem solve
(Level 2). Project scope and difficulty (Level 3).
Outcomes: Changes and trends in engineering practice on
EPICS’ direction (Level 3). Students’ professional
development and personalities (Level 3).
Subject: Effect of team composition on decision-making
(Level 1).
Tools: Which tools (technological, communications, and etc)
are best to mediate which facets of objective or different
teams? (Level 1 and 2)
Community: People/groups students work with (Level 1).
Communities that are formed (Level 1).
Division of Labor: Division of labor promoting problem-
solving, and mastery of skills or concepts (Level 1 and 2).
Rules: Rules concerning project teams (Level 1). Individuals
and community tensions and tool development (Level 2).
The consideration of quaternary contraction is necessary
when two or more project teams working on different projects
but for the same client. This is a subject of future research.
Performance Barriers and Enhancers
While individuals were satisfied with motivation and
self-concept levels, lower levels of feedback, expectations,
and rewards and incentives hindered the development of
cross-disciplinary team learning. Effective and efficient
methods to facilitate frequent feedback and clarification of
expectations is also a subject of future research.
Cross-Disciplinary Learning
Students on CDL teams felt they worked with teammates
from different disciplines; made decisions through consensus;
and appreciated others techniques and approaches with more
frequency than non-CDL teams. Two types of CDL
assessment were demonstrated in this research as useful in
understanding the evolution of CDL teams, factors
contributing to the cross-disciplinary team learning
framework and model.
The results of this project have implications for further
studies on validating the cross-disciplinary team model
(CDTL); 2) identifying effective performance, learning, and
performance support tools for cross-disciplinary teams; and 3)
designing a collaborative online learning environment for
teams. Better understanding of the social-cultural system
factors influencing team behaviors, as well as the best types of
performance support within such environments will help
designers create more contextually sensitive learning spaces.
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Colleagues at a large public university I recently visited are doing some excellent research on first-year engineering students—what attracted them to engineering, how they view engineering as a curriculum and career, how they feel about their first-year courses (it isn't pretty!), their confidence levels before and after those courses, and why the ones who drop out do so. I sat in on one of their weekly meetings, and one of them—an education professor—expressed bewilderment and dismay that with so much known about what makes teaching effective, engineering programs persist in using the same old ineffective methods. She wondered if there was any point in continuing research directed at improving a system this intransigent. I've heard the same thing from others engaged in educational reform—it's definitely an uphill battle, and it's easy to get discouraged when your focus is restricted to a single campus. Taking a broader view, though, things don't look that bad. Engineering education went through a major sea change once before, and the signs are that it is doing so again. I tried to offer some words of encouragement at the meeting and thought I'd repeat them here for readers engaged in similar lonely battles. A little history first. From the late 19 th century through the 1950s, engineering education was a combination of lecture and hands-on instruction closely tied to industrial practice, and the faculty consisted primarily of experienced engineers and consultants to industry. In the mid-1950s, America seemed to be falling behind Russia in the space program and calls were issued for an increased curricular emphasis on the mathematical and scientific foundations of engineering. In the years that followed, external funding opportunities for basic research skyrocketed, faculty started to be hired primarily for their potential as researchers, and most laboratory and field experiences disappeared from the engineering curriculum to be replaced by lectures on applied math and science. The paradigm shift from practice to science was essentially complete in most engineering schools by the early 1970s.
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