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Engineering Design practice is increasingly becoming a global activity where individuals, who are geographically distributed, work together as a team. Although the mainstream core of engineering design trains students to face teamwork from a co-located standpoint, existing studies point out the benefits and tradeoffs of distributed team training. This article explores the complexities of working in distributed teams by assessing distributed team experiences on three different continents. To achieve this goal, the study was organized in two stages. In the first stage, a framework was developed based on a yearlong mixed-methods study where four engineering teams from two prestigious universities in Chile and the U.S. worked together on open-ended problem-based challenges. Subsequently, the data from other 11 teams including distributed work among students in Chile, the U.S., and Finland, in the period spanning from 2016 to 2017, was collected and analyzed. A time tracking research instrument was created assessing how teams allocate their efforts within the design process and how this allocation varies across co-located and distributed teams. In addition, 10 semi-structured interviews were conducted with students from the first stage in order to triangulate the information. Findings show that distributed and co-located teams spend similar amount of time in convergent and divergent design activities. Moreover, evaluators identified improvements in the end solutions designed by students since there seems to be a cultural and academic complementation in the solutions proposed by distributed teams. All teams tend to use more time on convergent activities rather than divergent ones, especially when preparing presentations for a larger class group. Special attention should be paid on the convergent stages of teams’ design processes in order to provide the right educational scaffolding to facilitate learning. This study sought to shed a light on the possibilities of working with geographically distributed teams, and we found that, overall, the trade-offs are not significant.
Assessing the Work of Geographically Distributed Teams in
Engineering-Design: Time Allocation in the Design Process
as a Form of In-Class Analytics*
DILAB School of Engineering, Pontificia Universidad Cato
´lica de Chile, Vicun˜a Mackenna 4860, Stgo, Chile.
School of Engineering, Pontificia Universidad Cato
´lica de Chile, Vicun˜a Mackenna 4860, Stgo, Chile. E-mail:
Department of Mechanical Engineering, University of Puerto Rico, PR-108, Mayagu
¨ez, 00682, Puerto Rico. E-mail:
Engineering Design practice is increasingly becoming a global activity where individuals, who are geographically
distributed, work together as a team. Although the mainstream core of engineering design trains students to face
teamwork from a co-located standpoint, existing studies point out the benefits and tradeoffs of distributed team training.
This article explores the complexities of working in distributed teams by assessing distributed team experiences on three
different continents. To achieve this goal, the study was organized in two stages. In the first stage, a framework was
developed based on a yearlong mixed-methods study where four engineering teams from two prestigious universities in
Chile and the U.S. worked together on open-ended problem-based challenges. Subsequently, the data from other 11 teams
including distributed work among students in Chile, the U.S., and Finland, in the period spanning from 2016 to 2017, was
collected and analyzed. A time tracking research instrument was created assessing how teams allocate their efforts within
the design process and how this allocation varies across co-located and distributed teams. In addition, 10 semi-structured
interviews were conducted with students from the first stage in order to triangulate the information. Findings show that
distributed and co-located teams spend similar amount of time in convergent and divergent design activities. Moreover,
evaluators identified improvements in the end solutions designed by students since there seems to be a cultural and
academic complementation in the solutions proposed by distributed teams. All teams tend to use more time on convergent
activities rather than divergent ones, especially when preparing presentations for a larger class group. Special attention
should be paid on the convergent stages of teams’ design processes in order to provide the right educational scaffolding to
facilitate learning. This study sought to shed a light on the possibilities of working with geographically distributed teams,
and we found that, overall, the trade-offs are not significant.
Keywords: engineering design; teams; distributed teams; time allocation; time assessment; teamwork
1. Introduction
Engineering practice is increasingly becoming a
globally dispersed activity [1]. International teams
doing distributed work are now the norm in many
companies. The geographically distributed condi-
tion focuses on having participants, with specific
knowledge, to tackle a particular project while
working in different locations [2]. Despite this
trend, engineering professionals are usually not
equipped with the necessary skills to face this new
reality. This is partly due to the higher education’s
prioritization of theoretical knowledge over the soft
skills needed to work in distributed and multicul-
tural environments [3].
That said, there are some academic institutions
that offer courses on how to work in collaborative
and distributed teams providing hands-on experi-
ence [2, 4, 5]. These courses connect students from
different parts of the world to resolve, in a colla-
borative way, a real-world problem. Students gain
experience in working in distributed teams improv-
ing their professional skills, putting their knowledge
to practice and learning about how standards differ
across countries and cultures [2–4, 6]. However,
studies that examine the courses with geographi-
cally distributed teams [4] have identified challenges
regarding culture, language differences, time region,
and working habits.
In spite of these hurdles, or perhaps in response to
them, a particular field of research has focused on
investigating how geographically distributed teams
of students operate, using quasi-experimental meth-
ods [7–8]. These studies usually rely on giving the
same ideation task [7] to both co-located and dis-
tributed teams to explore the technological barriers
they encounter [8] and/or their effectiveness and
social differences [9]. However, more research is
needed regarding the complete course cycle and
the free evolving process of co-located and distrib-
* Accepted 19 November 2019. 399
International Journal of Engineering Education Vol. 36, No. 1(B), pp. 399–410, 2020 0949-149X/91 $3.00+0.00
Printed in Great Britain #2020 TEMPUS Publications.
uted teams in the context of an open-ended engi-
neering design challenge. This study examines time-
allocation and group dynamics of co-located and
distributed engineering students teams working on
open-ended design challenges. This study explores
the challenges and benefits of working in distributed
teams, assessing distributed team experiences from
three different continents, providing insights for
those interested in developing students’ skills for
working in co-located and geographically distribu-
ted teams in real-time to ensure their success during
the learning process.
2. Theoretical Framework
2.1 Definition of Distributed Teams
As in any other arising phenomena, the literature
has not yet reached a consensus on how to define a
working team that is not physically working
together. Jarvenpaa, Sirkka, and Leidner [10] refer
to these as global virtual teams and define them as a
‘‘temporary, culturally diverse, geographically dis-
persed, electronically communicating work-group
of members who think and act in concert within the
diversity of the global environment’’ (p. 792). Other
authors emphasize that virtual or distributed teams
are geographically dispersed, communicate using a
mix of technologies and are not dependent on the
difficulties caused by working in different time zones
[11–13]. The literature agrees that virtual team
conformation does not depend on distance; a cow-
orker that is 20 meters apart can be considered a
part of a distributed team [14].
2.2 Challenges of Implementing Distributed Teams
Teams are composed of members that work on
independent tasks for a larger common purpose.
Due to geographical distances, these teams rely
heavily on technology in order to communicate
[15, 16]. The communication challenges these
teams face are related to infrastructure or access
to technology to support virtual communication. In
this realm, virtual interaction makes relationships
more complex due to the limited vis-a
`-vis relations
and nonverbal language adding dependability on
communicative abilities of the participants [15–17].
Because of its importance, the literature emphasizes
the different means that students use to commu-
nicate to synthesize ideas, transfer knowledge, and
make decisions that impact the project [18] such as
email, instant messaging, and teleconferencing. Stu-
dies that examine how students communicate sug-
gest that the use of software that allows real-time
communication offers immediate problem resolu-
tion. At the same time, asynchronous media are also
considered fundamental for the formal knowledge
exchange [18]. Zaugg and Davies [17] indicate that
the most efficient strategy is to find free tools that are
also familiar to the students. In these cases, famil-
iarity and functionality become practical for these
types of team relationships.
Once the challenges derived from communica-
tion/software tools are tackled, the literature indi-
cates that language is another barrier to overcome in
virtual or geographically distributed teams. Since
most of the team members come from different
places, all of them might not speak the same
language (mother tongue) and they may have
issues when trying to express themselves in one
common tongue (e.g., English), which makes
group interactions more difficult [4, 19–21]. There
may also be such cultural differences that even when
the team members speak the same language, they
may not share codes and meanings related to the
cultural or country heritage. This situation could
create misunderstandings and interfere with group
cohesion [4, 15]. In this sense, it is important for
distributed teams to refine communicative practices
to reduce misunderstanding by clarifying or repeat-
ing information and limiting colloquial phrases [17].
Working with individuals that live in different
countries has the additional problem of accommo-
dating differences in time zones and students’ sche-
dules [19] which complicates the organization of
work. Organizational distance is one of the hardest
things to control because, in this kind of work,
synchrony among the parts is needed. Globally,
institutions start and finish their academic semesters
at different times, which makes coordinating group
activities a challenge [5, 21]. There needs to be an
effective monitoring of work and an integration of
the parts [20, 21]. A workplan that details when the
team is scheduled to meet, who is responsible for
different tasks, and when the tasks are to be com-
pleted and delivered is fundamental for team suc-
Academic challenges regarding differences in the
curriculum and the teachers’ roles are present in the
work of distributed teams in academia. The litera-
ture shows that universities have different forma-
tion programs, resulting in different levels of
knowledge. Hoda and his team [4] recommend not
to mix students with different academic back-
grounds and experiences (e.g., undergraduate and
graduate students) because it increases the chances
of miscommunication. Other authors indicate that
students should go through a previous leveling
experience so that they have a common set of
knowledge and skills [4, 19, 21]. On the other
hand, there are programs that involve multidisci-
plinary teams in which it is necessary that partici-
pants recognize the complementary nature of their
skills and professional knowledge [5]. In regards to
teaching instructors, Clear et al. [19] mention that
Constanza Miranda et al.400
they should be capable of coordinating and plan-
ning courses that could be carried out in one or more
locations. This implies the coordination of calen-
dars and the effective distribution of resources
across the classes. Students need instructors that
can manage this type of work, who are familiar with
communication tools, and that have the instruc-
tional knowledge and expertise to improve their
students’ skills.
2.3 Benefits of Implementing Distributed Teams
Although the literature reviewed exposes several
challenges regarding the effective use of geographi-
cally distributed teams, other research states that
there is not a large difference between co-located
and distributed teams. For example, Carrillo de Gea
and his collaborators [22] point out that co-located
teams perceive that their work is easier, faster, and
more productive than that of distributed teams.
Distributed teams, on the other hand, perceive
that their work is more effective. Paasivaara et al.
[6] have found that while the perception is that co-
located teams are more effective since they can
interact immediately, resolve questions quickly,
and generate more trust through face-to-face inter-
actions, there are no significant differences in the
way distributed teams organize their meetings,
manage their coordination, or divide their tasks.
Furthermore, Paasivara et al. [6] found that a vast
group of individuals prefer to work in distributed
teams because they perceived them to be better
organized, have fewer members and more clarity
of ‘‘who is doing what’’ [6].
There are a number of studies that go beyond
comparing co-located and distributed teams by
analyzing how engineering courses integrate this
type of soft skills training. Related to the commu-
nication challenges cited above, one of the biggest
benefits of offering soft skills team-based engineer-
ing courses is helping students learn how to effec-
tively communicate with each other in order to
reach a shared goal. Soft skills, such as non-verbal
communication and collaboration, become impor-
tant for successful completion of tasks and estab-
lishment of trust among team members. In addition,
Erez et al. [15] point out that having to interact with
individuals from other parts of the world improves
both foreign language communication skills and the
students’ awareness that they belong to a diverse
world with different economic, socio-political and
socio-cultural systems, languages, religion, and
Becerik-Gerber et al. [2] cite past research that
demonstrates that – if the program is correctly
implemented – collaborative learning increases the
acquisition and retention of knowledge, thinking,
communication abilities, and self-confidence. Soe-
tanto et al. [5] point out that students that partici-
pate in a university-based experience related to their
future profession have a competitive advantage
compared to those who did not go through the
experience. Therefore, the literature agrees that
these international experiences are beneficial to
students because they offer hands-on knowledge
of how these multinational, multicultural teams
work and coordinate. These programs also allow
students to gain professional skills in a real context,
giving them the chance to practice what has been
taught theoretically and to compare it with what has
been taught in other parts of the world [3, 6].
Although the literature has identified benefits and
challenges to distributed teaming, more research is
needed on assessment methods to facilitate the
actual observation of these patterns during class
periods. This study will seek to present an assess-
ment alternative to observe time-allocation
dynamics of groups, and by proxy, challenges,
opportunities and more importantly, times when
educational scaffolding is needed the most.
3. This Study: Engineering Design Teams
This article explores the complexities of working in
distributed student teams by assessing distributed
student team experiences from three different con-
tinents. To achieve this goal, we organized the study
in two stages. In the first stage (exploratory), we
developed a framework based on a yearlong mixed-
methods study where 4 groups of students from two
universities in Chile and the U.S. worked together on
open-ended problem-based challenges. All groups in
this stage worked on the following challenge: How to
improve mobility in aging populations? In the second
stage (confirmatory), quantitative data was collected
from other 11 teams, including distributed work
among students in Chile, the U.S., and Finland
spanning from 2016 to 2017. The numbers of team
members range from two to three students. Table 1
displays the teams characteristics.
The project-based course, Design and Systems
Thinking Lab at Pontificia Universidad Cato
(PUC) in Chile was paired with the project-based
courses in the U.S. (usually the engineering cap-
stone course) and Finland. The Design Systems
Thinking Lab is a semester-long course that is part
of the Engineering Design and Innovation major at
the undergraduate level. The course is aimed at
students in their third and fourth year. This major
is part of PUC’s common engineering sciences
curriculum and involves students that will later
specialize in mechanical or electrical engineering,
or computer sciences. Developed in 2013, the course
provides students with complex open-ended chal-
lenges posed by a local company or non-profit
Assessing the Work of Geographically Distributed Teams in Engineering-Design 401
organization that needs help with developing inno-
vative proof of concept prototypes.
Fig. 1 portrays the design process of the Design
and Systems Thinking Lab course, which spans over
18 weeks every semester. It entails divergent and
convergent processes where the teams go through
researching the context of the challenge to construct
a technological proof of the concept prototype with
a focus on the human interface. Grand deliverables
(GD) and deliverables (D) entail synthesis and team
presentation to an audience outside the project.
These are usually the areas were conflict most
commonly takes place [23].
As stated, this study entails two stages to explore
the entire process of an engineering design course
for several semesters. In the first stage, the metho-
dological framework was developed to assess time
allocation. Also, qualitative information was pro-
duced from interviews and through documentation
analysis. This part of the study was carried out
during 2014 for two academic periods at the
University of Notre Dame (ND), Indiana, U.S.,
and the Pontificia Universidad Cato
´lica (PUC),
Santiago, Chile. The second stage consisted of the
use of the same framework to research, in three
additional semesters, geographically distributed
courses in the University of Dayton (U.S)., Pontifi-
cia Universidad Cato
´lica (Chile), and Aalto Uni-
versity (Finland). This stage took place between
2016 and 2017. This study was primarily concerned
with the tradeoffs and gains of distributed teams in
terms of time efficiency and the resulting product, so
a mixed-methods approach was used to answer the
following research questions for the two stages:
Constanza Miranda et al.402
Table 1. Team characteristics
Team Year/Semester Condition Composition
Stage 1 Team 1 2014-1 Co-Located US
Team 2 2014-1 Co-Located Chile
Team 3 2014-2 Distributed US+Chile
Team 4 2014-2 Distributed US+Chile
Stage 2 Team 5 2016-2 Distributed US+Chile
Team 6 2016-2 Distributed Finland+Chile
Team 7 2016-2 Distributed Finland+Chile
Team 8 2016-2 Co-Located Chile
Team 9 2016-2 Co-Located Chile
Team 10 2016-2 Co-Located Chile
Team 11 2016-2 Co-Located Chile
Team 12 2017-1 Co-Located Chile
Team 13 2017-1 Co-Located Chile
Team 14 2017-1 Co-Located Chile
Team 15 2017-1 Distributed US+Chile
Fig. 1. Design process instructed in the Design and Systems Thinking Lab at PUC.
RQ1: What are the differences, if any, in how
students in distributed teams allocate time for
an engineering design project compared to stu-
dents in co-located teams?
RQ2: What are the differences, if any, in the result-
ing product of an engineering design project led
by a distributed team compared to a co-located
4. Methodology
This study used mixed methods in a concurrent
triangulation design [24]. In this type of design,
quantitative and qualitative data is collected simul-
taneously in order to gain better understanding of a
research problem. Our quantitative data was col-
lected from weekly time sheets to quantify differences
in the time co-located and distributed teams dedi-
cated to different design stages and working mod-
alities. Our qualitative information was collected
from semi-structured interviews [25] and course
documentation to describe the trade-offs and gains
from distributed work and its resulting products in
the context of the engineering design course under
study. Overall, 15 engineering teams were studied in
this research. Although both research methods were
focused on different aspects of the phenomena, they
converged in the overall understanding of group
dynamics in this particular case study.
4.1 Data Collection
Many courses that use geographically distributed
networks include educational instructions [26], but
there are few assessment frameworks for this peda-
gogic approach. Ha
¨ggman, Honda, and Yang [27]
propose a list of design activities that are timed to
better understand design outcomes. Thanks to a
previous long ethnographic study [23] it was under-
stood that team interaction in regard to the design
process deliverables occurred in three ways: (1) face-
to-face, (2) remote, or in (3) divided tasks. It was
important for the researchers to understand the
medium in which collaboration was sustained:
(a) Face-to-Face: the number of reported hours
that the team physically met or held a video-
conference to work on a certain task.
(b) Remote: the number of reported hours that the
team shared a common space or chatroom to
work together on a certain task.
(c) Divided Tasks: the number of reported hours
that the team worked on tasks that had been
divided up between the members, such that
each member took ownership on a particular
Assessing the Work of Geographically Distributed Teams in Engineering-Design 403
Fig. 2. Timesheet example. Adapted from [27].
Using Ha
¨ggman, Honda and Yang’s [27] design
activities and Miranda’s [23] insights, a time-alloca-
tion self-reported team timesheet was developed.
Fig. 2 shows the final version of the instrument. It
was applied weekly to the students. Timesheets were
handed to the teams of students each week during
one compulsory class period. They were printed in
black and white and collected by the teaching
assistant at the end of the class. In order to achieve
a 100% response rate, the act of responding was part
of the course responsibilities. In the first stage of the
research there were differences in the number of
lecture classes, so fewer timesheets were reported at
PUC compared with the second stage (eight weeks
instead of twelve weeks).
Finally, a total of 10 interviews using anthropo-
logical techniques [28], were conducted with stu-
dents of the first stage. Some of the interviews were
conducted face-to-face and others where under-
taken remotely using videoconferencing tools. The
interviews lasted between 20 and 40 minutes each.
The Design Lab course works under the policy of
acquiring informed consent to engage in a contin-
uous ethnographic data collection process for
assessment purposes. Apart from the interviews
done by a third party (research assistant), self-
reported information was collected in the form of
team blogs. Throughout the course, videos and
pictures were taken to document the processes.
These additional data sources allowed us to trian-
gulate the information coming from the interviews
in order to achieve qualitative validity. They also
complement the quantitative data obtained from
the weekly timesheets.
4.2 Analysis Plan
In order to answer RQ1, quantitative data was
analyzed to explore differences between distributed
and co-located teams in reference to time allocation
in both research stages. First, a descriptive statistic
approach [29] was adopted to compare timesheets
results in order to organize and show main descrip-
tive results. Then, design activities were grouped as
convergent, divergent or other activities. The idea
was to analyze whether more time was dedicated to
convergent activities, where conflict was more likely
to occur [23]. Considering the limitations to statis-
tical power of the low sample size, two-sample t-
tests were used to identify significant differences in
time allocation reports among co-located and dis-
tributed teams. Cohen’s D was used to estimate
effect size if a difference was detected. Finally,
statistical power was estimated using power.t.test
function in R (statistical software) if any statistical
difference was found.
In order to answer RQ2, qualitative data was
analyzed to critically examine and compare end
results in research stage 1. The research focus was
exploratory [25] aimed at linking raw data to the
research question at the early stage research [30].
Raw data from both the interviews and documenta-
tion was critically discussed within the research
team, complying with standards of anonymity and
confidentiality. Further analysis is needed to iden-
tify overall themes and theoretical relations, but an
initial process was considered appropriate to
describe main characteristics of the end-results.
5. Results
RQ1: What are the differences, if any, in how
students in distributed teams allocate time for
an engineering design project compared to stu-
dents in co-located teams?
Table 2 displays overall descriptive results of this
research. Because the emphasis of this research was
set on how students allocate time among different
activities and through different interaction med-
iums, percentages were analyzed over total hours.
Time allocation percentages help us to understand
priorities ratios among activities. In the first seme-
ster of 2014, T1-U.S. reported 14 timesheets
whereas T2-Chile reported only nine timesheets
because the course was not mandatory at the time
and its structure included four leveling weeks not
related with the overall project.
From a descriptive standpoint, a lot of diversity
can be observed among groups with no obvious
trend to distinguish them. Nonetheless, some obser-
vations can be made about groups’ similarities. The
activity with the highest time allocation percentage
was ‘‘presentation preparation’’ with an average of
23% of the time used. Other high percentage activ-
ities were ‘‘building’’ (10%) and ‘‘benchmarking’’.
There could be different explanations for these
results. For instance, these activities can be regarded
as difficult by the students, or the stakes regarding
their grades could be higher. In any case, they can be
considered as highly critical activities for students
and thus, special educational support may be
It can also be identified that some activities show
notable discrepancies among teams, but not neces-
sarily related to team’s distribution. ‘‘User inter-
action’’ consumed proportionally more time to
students in 2014. In the following years, this empha-
sis tended to disappear. This is probably best
explained by the focus of the instructor rather
than changes in teams’ inner characteristics. In
respect to interaction formats preferred by students,
it can be observed that ‘‘face-to-face’’ interaction
was on average the most utilized medium (55% of
time allocated). In most cases, ‘‘remote’’ interac-
Constanza Miranda et al.404
tions did not reach double digits. Exceptions were
both distributed (T4, T6 & T15) and co-located
teams (T10 & T13). Research presuppositions
were that a trend would be found among distributed
teams where ‘‘remote’’ interaction was greater than
face-to-face interaction. This presupposition did
not gain support from this data.
Miranda [23] argues that, in dealing with open-
ended problems, convergent activities tend to pro-
voke more conflict among teams. The research team
was interested in finding if any differences could be
found in time allocated between divergent and
convergent activities of the engineering design pro-
cess. Divergent activities are characterized by open-
ing new possibilities or adding complexities to the
project. Convergent activities, on the contrary are
related to reducing information and committing to
one set of possibilities. We categorized each of the
design activities proposed by Ha
¨ggman, Honda.
and Yang’s [27] as either convergent, divergent or
other. Table 3 displays our proposed categorization
and justification.
Having categorized activities as divergent or
convergent it is possible to compare co-located
and distributed teams on how they allocate time in
both types of activities. Table 4 show two-sample T-
test results comparing co-located and distributed
teams. Homoscedasticity assumption was checked
through a Barlett test. Results showed that samples
shared equal variances for both convergent (K
2.9, df = 1, p-value = 0.08) and divergent (K
= 0.6,
df = 1, p-value = 0.4) activities. Thus, t-tests utilized
assumed equal variance.
Table 4 shows that no significant differences were
found between co-located and distributed teams in
neither convergent nor divergent activities. This was
surprising as differences were expected, especially in
how teams allocate time in convergent activities.
These results are consistent with the aforemen-
tioned descriptive analysis where no clear pattern
was identified separating co-located and distributed
teams but rather patterns were found assimilating
them. For both team typologies, convergent activ-
ities were found to have greater means than diver-
gent activities. In this study case, all teams allocated
more time to convergent activities.
Overall, statistical results show no particular
differences between co-located and distributed
teams. Therefore, it seems that for students, there
is no extra workload when participating in a dis-
tributed team. On the other hand, timesheets served
as an assessment method for engineering design
teams to describe their learning journeys, efforts
and priorities. From a qualitative standpoint, this
assessment tool was reportedly useful for the teach-
ing team to monitor the different team interactions.
There is perceived potential to help practitioners in
evaluating whether their educational emphasis cor-
Assessing the Work of Geographically Distributed Teams in Engineering-Design 405
Table 2. Percentage of time allocation according to design activity and interaction medium
Year 2014 2016 2017
Team T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 Average
User interaction 14 21 14 18 8 6 4 3 0 7 4 6 2 4 5 8
Market research 5 4 6 8 10 7 3 13 6 3 10 2 5 5 4 6
Benchmarking 1 4 5 12 13 7 21 8 21 13 26 19 15 20 8 13
Concept Generation 12 8 12 13 0 0 0 2 0 2 0 0 0 0 0 3
Concept Selection 0 3 8 8 6 15 3 13 5 12 5 8 8 7 19 8
Design: Sketching 15 9 7 9 3 8 3 11 3 6 11 2 1 7 7 7
Design: CAD 0 1 3 3 0 7 1 6 8 4 1 5 2 7 4 3
Design: Else 2 0 3 5 2 1 0 5 12 8 11 9 5 1 4 5
Building 0 12 10 7 12 18 33 10 7 4 5 14 3 7 6 10
Business Plan 0 0 2 1 5 7 4 3 3 6 3 2 2 6 7 3
Presentation Prep. 44 35 20 12 23 25 23 13 28 25 21 22 14 21 25 23
Administrative 6 5 9 3 10 0 4 9 7 7 2 3 28 4 9 7
Other 9 1 0 0 9 0 1 3 0 5 0 6 13 10 0 4
Face to face 68 34 66 63 57 40 42 67 60 52 61 45 74 48 47 55
Remote 6 4 8 14 6 23 7 3 0 19 3 6 17 4 35 10
Divided tasks 27 62 27 23 37 37 51 30 40 29 36 48 9 47 18 35
Note. Values represent intra-group percentages. Decimal numbers were rounded. Light color emphasis represents distributed teams.
Regular color emphasis represents average results
relates to teams’ time allocation. In sum, timesheets
could help to portray broadly the students’ experi-
ence with little cost to teachers. To generate more in-
depth analysis, triangulation is needed with other
research approaches.
RQ2: What are the differences, if any, in the result-
ing product of an engineering design project lead
by a distributed team compared to a co-located
During the first research stage, the distributed
teams from ND and PUC started with a user-
centered research activity that involved them in a
context assessment using applied qualitative
research methods. This process was carried out in
a divided way in each of their home countries.
According to their presentations and course doc-
umentation, ND students realized that senior citi-
zens wanted to embrace an active lifestyle, so they
decided to build products that would help seniors
better accomplish daily activities in an independent
way. Fig. 3 shows the developed solutions – a
‘‘lifting cushion’’ and a ‘‘product to go up the
stairs’’. At the same time, the group at PUC under-
took the same research process in their home
country. After doing a thorough analysis of their
data, they reported through the course deliverables,
that the local aging population in Chile (in a lower
social strata) had to continue working after their
retirement age. This work entailed hard labor, such
as gardening or sweeping. The team developed a
universal ergo handle that minimizes lower-back
pain for these aging workers. Fig. 3 displays the
projects developed in this first stage. The pictures
show that they are either too biased on the
mechanics (ND’s lifting cushion and product to go
up the stairs) or too biased on the end-user design
(PUC’s ergo handle). As the instructors pointed out,
this reflects the kind of program bias on technical
aspects of mechanical engineering and human-cen-
tered design respectively. The cushion and bar to go
up the stairs work perfectly, but they show a lack of
fulfillment of the design requirements in the sense of
user experience and ergonomics. In the case of the
ergo handle, the product provides a great user
experience and is ergonomic, but it breaks because
the structure is not correctly mechanically designed
for the type of plastic used.
In the next semester, students from ND and PUC
faced the same challenge as the above-mentioned
co-located teams. The teams underwent a similar
design process for context assessment. Nonetheless,
due to their geographically distributed nature, the
students had to combine user research done in two
different cultural contexts (U.S. and Chile). In
addition, the teams had to combine their use of
Constanza Miranda et al.406
Table 3. Design activities as divergent or convergent
Design activity Type Rational
User Interaction Divergent They identify new information
Market Research Divergent They identify new information
Benchmarking Divergent They identify new information
Concept Generation Divergent They create new ideas
Concept Selection Convergent They select ideas
Design Sketching Divergent They create new ideas
Design CAD Other Not necessarily creation or selection
Design Anything Else Other Not necessarily creation or selection
Building Convergent They select ideas
Business Plan Convergent They select ideas
Presentation Preparation Convergent They condense information
Note. Adapted from Ha¨ggman, Honda. and Yang [27]. Categorization of activities as convergent and divergent.
Table 4. Welch two sample T-test results comparing convergent/divergent time allocations (in percentages)
Mean Two-Sample T. Test
Activity type Co-located Distributed T df p-value
Convergent 41.13 50.02 –16.313 13 0.1268
Divergent 36.76 36.95 –0.0322 13 0.9748
Note. T-Value for two-sample t-tests comparing percentages of time usage in convergent and divergent design activities in co-located and
distributed teams. *p < 0.05, **p < 0.001, ***p < 0.001.
tools and expertise to solve the opportunities iden-
tified in the field. After embracing a rigorous diver-
gent and convergent design process, the teams
developed the solutions illustrated in Fig. 4. The
products are: an intelligent knee brace that tracks
the recovery process of the user and a traveling
thrombosis prevention massager. Both projects
had mechanisms and technology that worked.
Both projects were commended by the public
attending the final presentations (teaching and
industrial partners) due to their ‘‘usable’’ and ‘‘tech-
nically advanced’’ designs. The travel thrombosis
prevention massager won a ‘‘demo day’’ university
contest to represent the university at an interna-
tional competition and appeared in the local new
paper as a successful device.
After discussing interviews and documentation,
the research team concluded that the projects of the
distributed teams were able to combine technology,
mechanics, and usability. Students tended to per-
ceive this complementation too. As one student puts
‘‘If we worked with a team that was at ND we would have
more easily communicated in person. We would have
missed out on the different perspective that the PUC
students had to offer’’ (US student).
It was also notable that students were able to
identify the educational focus on engineering or
design in these two institutions. One of the students
‘‘They spent more time interviewing participants and
gathering data. I think the ND students spent additional
time later in the project on trying to get the technical
aspects of the project to work’’ (US student).
Their Chilean counterparts agreed:
‘‘The development of most of these ideas was a distrib-
uted work. The mechanical part was assigned to the boys
in Notre Dame; they had to test different ways to make
the idea technically feasible and let us know every time
they had a difficulty. The design part was assigned to us in
Chile, and then our ideas were discussed again in a
cooperative work’’ (Chilean student).
Divergent activities, such as idea generation and
research proved to be more memorable to distrib-
uted teams than convergent ones. When asked
about the main contribution of the distributed
part of the teams, most of students pointed out the
idea generation.
In contrast, the projects in Fig. 3 are either too
mechanical or barely usable. Final instructors’
evaluations of the deliverables coincide with our
findings. The distribution of hours in the distributed
teams shows that T3 and T4 dedicated a good
number of hours to user interaction and research.
Even though this is a divergent step in the design
process, it could be explained because, as students
Assessing the Work of Geographically Distributed Teams in Engineering-Design 407
Fig. 3. Products developed by the co-located teams.Lifting cushion, product to go up the stairs and ergo handle (from left to right).
Fig. 4. Products developed by the co-located teams. Intelligent knee brace and traveling thrombosis
prevention massager (from left to right).
reported, they had to match and consolidate the
research done in both countries. On the other hand,
this user research stage was not a focus of capstone
in the U.S. as compared to the course in Chile. This
also happened the other way around. The mechan-
ical engineering program was biased towards the
technicalities in the mechanics. Because of the
results, it seems that the course methodologies
cross-pollinated each other, combining their best
in order to achieve better solutions that fulfilled
both program requirements.
6. Discussion
This study looked at the way co-located and geo-
graphically distributed teams worked when facing
an open-ended challenge in an engineering-design
course. The article looks to advance the literature on
this topic by studying 15 teams of students where six
of them were geographically distributed and
included individuals from Chile, Finland, and the
U.S. By using a framework that involved the mon-
itoring of hours invested per design stage, course
documentation, and qualitative data collected in the
form of interviews, we were able to tackle the
proposed research questions through exploration.
We found out that there are no significant differ-
ences in how students in distributed teams allocate
time in convergent stages compared to co-located
teams. This was a surprise as our initial presupposi-
tions was that convergent activities would elicit
conflict, and in turn, more time-usage. However,
the results showed that all groups used proportion-
ally similar amount of time on these activities. The
number of hours the teams dedicated to the various
project tasks was more related to when a deliverable
or grand deliverable was due, and it was usually
because the team was going from a divergent phase
to a convergent one – the group needed to negotiate
one vision, one prototype, or one concept. Other
hypothesis may be evaluated in order to explain the
results. For instance, differences and commonalities
may respond to the particular instructor emphasis,
characteristics of the design challenge or even
student’s personalities and personal interests. The
purpose of this study was not to isolate the effects of
team distribution, but rather to explore whether the
effect of team distribution was strong enough to be
salient in non-experimental conditions. Further
studies should test effects using more advanced
statistical procedures for controlling teacher, stu-
dent and tasks characteristics.
Regarding our second research question, we
observed that complementation was obtained and
perceived between US and Chilean students. Stu-
dents appreciated the collaborative experience
because of the different educational focus of the
partnering institutions. ND was identified as prior-
itizing technical aspects of engineering and PUC
was identified as prioritizing qualitative research
and people-centered design. Researchers observed,
however, that the result depended completely on
team performance, communication, and commit-
ment to the project. We also found that a recombi-
nation of knowledge from the two different
programs occurred, but the team could only take
advantage of it when performing at its best.
The identification of critical periods for distrib-
uted teams is key for practitioners in order to
provide special attention or ‘‘scaffolding’’ [31]
needed to produce learning. Timesheets could be
used as a broad thermometer to assess how fluid is
team cooperation, if teams are held back, if not
enough time is being spent on user research and so
on. The research team notes that they have helped
them provide better care of students without intru-
sively interfering in the team’s autonomy. The
balance between negligence and overprotection is
usually challenging for instructors, so having a cost-
effective tool to assess when to intervene could
prove helpful to teach both distributed and co-
located teams.
7. Conclusion
The research team learned that co-located and
distributed teams divide tasks depending on their
mastery. Related to time, students have the percep-
tion that they invest more time if distributed, so
monitoring becomes key to overcoming students’
perception of workload. Along the same lines,
monitoring serves as a tool for the teaching team
in raising early alerts on team performance through-
out the process. When working with international
teams, it is crucial to consider the timelines of the
courses – that they start and end more or less at the
same time. Teams need to be small in order to
coordinate virtual meetings. Physical face-to-face
instances may improve the interaction of the inter-
national teams, but they do not ensure success. It is
important to have a common process to bridging
teaching experiences due to the differences in the
deployment of the engineering programs. Having a
common challenge that is the same across cultures is
central to the project. On the other hand, benefits
perceived by students are varied in the realm of
communication skills. Three benefits that the stu-
dents reported and that we found relevant to the
future of engineering education and engineering
practice are: (1) the ability to overcome frustrating
situations and develop tolerance towards other
culturally-driven procedures, (2) the cross-pollina-
tion of the different focuses and techniques that
engineering programs impart, and (3) the develop-
Constanza Miranda et al.408
ment of an international network without spending
the monetary resources on a study abroad program.
This study sought to shed a light on the possibilities
of working with geographically distributed teams,
and we found that, overall, the trade-offs are not
significant. So why not?
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Assessing the Work of Geographically Distributed Teams in Engineering-Design 409
Constanza Miranda is an assistant professor at Pontificia Universidad Cato
´lica de Chile’s (UC) School of Engineering
where she directs the DILAB Engineering Design Initiative. She holds a PhD in Design with a focus on anthropology from
NC State University. While a Fulbright grantee, she worked as a visiting researcher at the Center for Design Research at
Stanford. As an entrepreneur, her focus is on the development of user centered biomedical devices, Her research work is
focused mainly in the area of bio design, engineering-design education and the use of anthro-design methods to bridge
social inequalities. Past experiences involve work in industry such as Cooper San Francisco and Continuum Milan.
˜aki Gon
˜iis an educational psychologist from the Pontificia Universidad Cato
´lica de Chile, with academic certification in
Economy. He is a lecturer and researcher at DILAB (School of Engineering UC). In DILAB UC he researches on topics
such as engineering education, social epistemology and teamwork. He is also the assistant director of the Cogency Journal
of Reasoning and Argumentation. He is interested in research, theory and application of interdisciplinary social sciences,
with emphasis on the intersection of educational psychology, philosophy and STS.
Isabel Hilliger is currently the Associate Director for Assessment and Evaluation at the Engineering Education Division in
the Pontificia Universidad Cato
´lica de Chile (PUC). Isabel received a BEng from PUC and an MA in Education Policy
from Stanford University. She is currently pursuing a PhD in Computer Science at UC School of Engineering. Her research
theme is about methodological frameworks and learning analytics tools to facility quality assurance and curriculum
´E. Lugo, PhD is an Assistant Professor in Mechanical Engineering at the College of Engineering of the University of
Puerto Rico at Mayagu
¨ez (UPRM). He obtained a Doctor of Philosophy degree from the University of Notre Dame in
Notre Dame, IN, in 2014; a Master in Science degree and a Bachelor of Science both from the University of Puerto Rico at
¨ez, in 2009 and 2007 respectively, all in the field of mechanical engineering. Currently, Dr. Lugo works as assistant
professor in the mechanical engineering department of the University of Puerto Rico at Mayagu
¨ez. As a professor, he
teaches courses related to design and optimization, design thinking, design of machine components and engineering
systems design.
Constanza Miranda et al.410
... To perform this estimation, the Engineering Education unit proposed to adapt a weekly paper-based timesheet that was being used to estimate student time-on-task in subjects that are part of the engineering design major at UC-Engineering [12]. This timesheet was adapted from previous work of Hägman, Honda and Yang [13], who measured the time that students perceive they spent on different activities that are part of a product design subject, including: concept generation, prototyping, and presentation preparation. ...
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