ENE695 – HARNESSING INDUSTRY EXPERTISE
Global Engineering Expertise Library
Improving Engineering Practice Across Cultures
Student Investigator
Andrea Mazzurco
Engineering Education PhD student
701 West Stadium Ave.,
ARMS Hall
Purdue University
West Lafayette, IN
[email protected]
PI
Brent K. Jesiek
Assistant Professor
School of Engineering Education
701 West Stadium Ave.,
ARMS Hall
Purdue University
West Lafayette, IN
[email protected]
December 3rd, 2012
Executive Summary
A large part of industries wealth and competiveness depends on the knowledge, skills, and
attributes of their human capital. The rapidly shifting of U.S. workforce demographic and the
high turnover rate is threatening to alter the competiveness of U.S. industries. Thus, it is
becoming imperative to effectively retain the tacit knowledge of experts and to transfer it to
lesser experienced workers who will replace them. However, there is little or none effort to retain
engineers’ ability to work effectively across countries and cultures. Globalization is an ongoing
process that has begun several decades ago; as a consequence, engineers have gained
competencies that allow them to effectively perform in international settings. Moreover,
globalization trends are intensifying rapidly, and future engineers will have to practice in an
increasingly high cross-national context. Hence, it is vital to capture the knowledge of global
engineering experts and to make it available to any engineers about to undertake cross-national
projects.
This research proposes to provide a process that will allow industries to retain and transfer
engineers’ ability to work effectively in cross-national/cultural contexts. The research objectives
of this study aim to (1) identify Global Engineering Experts (GEEs), i.e., engineers who are able
to function effectively in cross-cultural contexts; (2) collect experiences from a selected small
sample of GEEs and create Global Egnineering Critical Incidents (GECIs); and (3) prototype a
searchable Global Engineering Expertise Library as a mean to share GEEs knowledge in form of
GECIs.
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This research will begin by administering a survey that aims to assess global engineering
competence to employees within the firm. In addition, information about engineers’ prior
international experience will be collected. So that, engineers with scoring high in the survey and
having an extensive international experience will be labeled as GEEs. At this point a list of GEEs
will be delivered to the industrial partner. Then, a small selected sample of GEEs will be
interviewed to elicit their experiences and based on this data GECIs will be collected. Finally, a
prototype of a library that leverages the Case-based Reasoning methodology will be developed
and tested. At the end of the project, the industrial partner will receive the GEEL prototype and
directions on how autonomously execute all the above tasks and further populate the library. The
proposal is idealized as a 10 month project, during which all the above tasks will be performed.
The budget of the proposal is 48,687$, which includes the Graduate Research Assistantship of
the student investigator who will work at rate of 10 hours/week; the funding for paying an
undergraduate programmer to help develop and test the GEEL; the salary of the PI who will
function as a consultant, and other expenses.
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Problem Statement and Research Objective
In today’s globalized economy, knowledge is an important resource and it constitutes a
large part of a company’s wealth (Chen, Zhu, & Xie, 2004). Thus, industries depend enormously
on the competencies, attributes, and creativity of their human capital. Yet there are several events
that can cause the loss of valuable knowledge. One of the major reasons for knowledge loss is
the retirement of the baby boom generation. The 2000 U.S. census estimated that almost 83
million individuals are 48 to 66 years old (L’Allier & Kolosh, 2005). This means that a large
portion of the experienced U.S. workforce has and is already retiring and all the knowledge and
experience that they have accumulated over a lifetime is going with them – unless it is somehow
captured or transmitted. In addition, Nelson and McCann (2010) point out that other losses of
knowledge occur when talented employees decide to leave their job position to move to other
companies or start their own business. Given that the turnover rate is around the 40% in industry
and government (The Bureau of Labor Statistics); industries can fail to retain important
knowledge if they do not deploy effective strategies (Casher & Lesser, 2003).
Moreover, some kinds of knowledge are not routinely captured through existing
knowledge management processes and systems. For example, international marketplace forces
engineers to work with very diverse engineering cultures and standards (Swearengen, Barnes, &
Coe, 2002). Hence, many engineers in industry have been involved in cross-national projects and
have gained lots of experience from such situations. However, their experience and knowledge is
often not formalized and documented, and this kind of expertise is often lost once these global
experts move on to other projects and positions.
While there is a wide understanding of the importance of human capital and the retention
of experts’ tacit knowledge, there is little awareness regarding the need to capture experts’ ability
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to work across countries and cultures. Moreover, while the academic community agrees on the
need to educate globally competent engineers (Ayokanmbi, 2011; J. R. Lohmann, Rollins, &
Joseph Hoey, 2006; Parkinson, 2009), only a small percentage of U.S. engineering students has
international experience (Lohmann, 2003). As a consequence, the new generations of
engineering graduates who will replace retiring experts are not yet prepared for cross-cultural
technical work environments and will need to be trained and mentored to excel in such situations.
Hence, it becomes imperative to capture the knowledge of engineers that have extensive
international experience and to make it available for other engineers that will have to practice in
the global workplace.
The purpose of this research proposal is to provide a process that will allow industries to
retain and transfer engineers’ ability to work across countries and cultures. This will be done by:
1. Identifying Global Engineering Experts (GEEs), i.e., engineers who possess
competencies that allow them to work effectively with international partners;
2. Collecting Global Engineering Critical Incidents (GECI), i.e., stories that depict puzzling
situations involving both technical and cross-cultural aspects; and
3. Creating a prototype of a searchable Case-Based Library that will contain all of the
GECIs, so that GEEs’ experiences will be accessible at any time by engineers about to
undertake an international project, and by human resources managers responsible for the
training of workers going abroad.
Identification of Global Engineering Experts (GEEs)
The first step of this research proposal regards the identification of engineers that had
extensive experience of working across countries and culture and therefore have developed skills
that allow them to solve puzzling cross-cultural situations in engineering context. This will be
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done by the use of a survey meant to measure cross-cultural competencies and by collecting
background information, especially length and number of international experiences, of engineers
within the firm. Thus engineers who both scores high on the survey and had extensive prior
experience will be labeled as GEEs and will be the subject of the second phase of this proposal.
One of the first conceptual model of global engineering competence comes from the
work of Lohmann, Rollins, & Hoey (2006). In their model, they defined global competence as
the:
“the ability to work knowledgeably and live comfortably in a
transnational engineering environment and global society”
(Lohmann, Rollins, & Hoey, 2006, p. 1).
In addition to a definition, they also provide a conceptual model to define such competence. The
model is based on five elements: (1) proficiency in a second language, (2) international
coursework, and (3) an immersive international experience which should be combined in a
coherent program that (4) ties the elements together and (5) integrates them within the student’s
major (Lohmann, et al., 2006).
Rather than giving a precise definition of global competence, Downey et al. (2006)
provide a learning criterion to guide the creation of and to assess students’ learning from the
Engineering Cultures course that they developed at Virginia Tech and Colorado Schools of
Mines. In this course students learn about the historical and cultural aspect of engineering
profession in several countries. The learning criterion is stated as follows:
“Though course instruction and interactions, students will acquire
the knowledge, ability, and predisposition to work effectively with
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people who define problems differently than they do” (Downey et
al., 2006, p. 110)
The learning criterion also comprises three learning outcomes. The first component
focuses on knowledge. In the authors’ opinion, successful global experiences should allow
students to acquire and demonstrate understanding of how engineers and non-engineers may
differ in their work and in the meaning their work has for their careers and lives. The second
learning outcome is ability. Globally competent engineers should be able to go beyond the pure
knowledge of similarities and differences among engineers and non-engineers of other countries.
They will have to demonstrate an ability to apply the knowledge acquired into everyday practices
and behaviors of engineering work. Finally, the third learning outcome is predisposition. Such
outcome is more difficult to fully identify and assess. In this case, the term used by the authors
does not refer to the personal character of individuals, but to “learnable tendencies” or “patterned
actions” that allow students to treat co-workers from other countries as people who have
knowledge and value.
Diverging from approaches that consider learning outcomes and conceptual models, other
authors do not give a precise definition, however they provided a list of attributes that describe
what it means to be globally competent. For instance, Parkinson, Harb, and Magleby (2009)
propose 13 dimensions or attributes of global competence. Such attributes were based on
previous definitions, experience with the authors’ study abroad programs, and stated objectives
of courses and programs which prepare students to be globally competent. From a survey,
Parkinson, Harb, and Magleby (2009), found out that, among the 13 attributes presented, the
following five are conisdered especially important by both academic and industrial
representatives:
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1. Appreciation of other cultures. This attribute refers to the ability to avoiding the idea
that one’s own culture is superior to all others. Thus, engineers need to develop
appreciation and sensitive to others cultures.
2. Proficiency to work in or direct a team of ethnic or cultural diversity. This attribute
refers to the ablity to deal with the problems arising when working in a team
characterized by diverse cultures. This dimension is also strongly related to the
following attribute.
3. Communication across cultures. This attribute includes an understainding of cultural
differences regarding status, formality, saving face, directness, and the meaning of
specific words.
4. Opportunity to practice engineering in a global context. This dimension focus on
practise and experience, rather than on knowledge or understanding.
5. Ability to deal with ethcial issues arising from cultural or national differences. Ethical
issues in this case range from bribes and tax evasion to safety standards.
Finally, Huff, Abraham, Zoltowski and Oakes (2012) collected the attributes of the
discussed models and sorted them accordingly to three psychological dimensions suggested by
Jesiek, Shen, and Haller (2012): cognitive, behavioral and attitudinal. As illustrated in table 1,
the cognitive dimension of this framework refers to an engineer’s knowledge of cultural
differences; the behavioral dimension refers to an engineer’s flexibility and adaptability to cross
cultural settings; and the attitudinal an engineer’s openness and respect of cultural differences.
Although no instrument has been developed to measure all these attributes specifically for
engineers, scholars in other disciplines have developed surveys that presents features very
similar to the one presented so far. One such instrument is the Cultural Intelligence Scale (CQS)
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developed by Earley and Ang (2003). CQS is a 20-item instrument which uses a 7 point linkert
scale to measure individuals’ Cultural Intelligence (CQ), i.e, “the capability to function and
manage effectively in culturally diverse settings”(Ang, & Van Dyne, 2008).
CQ is measured
across four subscales:

Metacognitive CQ: an individual’s consciousness and awareness during interactions with
those from different cultural background,

Cognitive CQ: an individual’s knowledge of norms, practices, and conventions in
different cultural settings,

Motivational CQ: an individual’s capability to direct attention direct attention and energy
toward cultural differences

Behavioral CQ: an individual’s capability to exhibit proper verbal and nonverbal actions
when interacting with people.
Hence, the CQS is a good fit for this phase of the proposal because it measure similar
psychological dimensions of Huff et al. (2012) framework. Moreover, it is freely reusable in
unmodified forms, can be completed quickly, and has been proven to be a reliable and valid
instrument (Van Dyne, Ang, & Koh, 2008).
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Table 1. Global Competencies (Huff et al, 2012)
Creation of Global Engineering Critical Incidents (GECI)
Once GEEs are identified, the project enters a second phase which consists of capturing
the experiences of GEEs and transforming them into GECIs that would then be collected in an
online library. Stories will be elicited using semi-structured interviews. In order to gain the most
from these interviews, Jonassen and Hernandez-Serrano (2002) suggest following three simple
steps. First, engineers that were involved in global projects must be contacted and a meeting
must be organized. Second, at the beginning of the interview, a story in a cross-cultural setting
should be shown to the expert. The story must have both technical and cultural aspect, such as
the one in figure 1 (from Jesiek et al. 2011). Third, the interviewer should ask the engineers to
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remember similar situations he/she experienced and guide him/her in retrieving the most relevant
information. If any GEEs will not be available to be interviewed, a phone meeting or an online
form that follows the same structure of the interview will be utilized.
As an employee in a large multinational corporation, you are temporarily assigned to your
company’s branch operations in Shanghai, China. Your work team consists of three
Chinese engineers, all at about the same rank as you. Your team reports to an engineering
manager, who is also Chinese. In a recent team meeting, your manager proposed a solution
to a difficult quality control problem. However, you feel you have a much better solution to
the problem. How would you deal with this situation?
Figure 1. Global Scenario (Jesiek et al, 2011)
The stories collected from the interview will be formalized as GECIs. Critical incidents
are well established psychological tools that “consist of a set of procedures for collecting direct
observations of human behavior in such a way as to facilitate their potential usefulness in solving
practical problems” (Flanagan, 1962). In the case of this proposal, each GECI will consist of a
story describing especially puzzling, notable, or significant cross-cultural interactions in
engineering settings, as well as an explanation of proper behaviors or approaches to follow in
such situation. Such stories will then be available to other engineers that will work in
international setting. Thus, the engineers will rely on past experiences of GEEs, rather than
abstract theoretical frameworks about cross-cultural interactions.
The choice of using the above methodology for capturing GEEs experience and using
stories to transfer them is based on considerations about expertise. It can be argued that an
expert’s mind can be viewed as a structured library of information that is organized around
abstract concepts and is contextualized in the experts’ past experiences. Moreover, experts can
retrieve such knowledge with little effort and can find meaningful patterns that help them solve
new problems. In fact, Stepich (1991) affirms that experts have a “large library of information in
the form of condition-action units”. Thus, when experts are faced with an external stimulus they
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retrieve the appropriate response from their internal library (Stepich, 1991). Ross (1986, 1989)
demonstrated that people learn a new skill by using what they have learned in the past from
solving a problem and applying it to the new situation. For instance, this is the case of expert fire
commanders, tank commanders and systems designers that heavily rely on past experiences to
wrestle successfully with new difficulties (Klein & Calderwood, 1988; Kolodner, 1992).
Additionally, research demonstrates that both car mechanics and GTE engineers troubleshooting
phone switching networks use past experiences to tackle new problems (Kopeikina, Brandau, &
Lemmon, 1988; Lancaster & Kolodner, 1988; Kolodner, 1992). Finally, engineers have
especially been shown to use lessons learned from previous experiences to master puzzling new
situations (Polkinghorne, 1988; Kolodner, 1992).
Creation of a Case-Based Library of GECIs: the Global Engineering
Expertise Library (GEEL)
In the final phase of this project, the GECIs will be collected in a library or database that
reflects the way experts think. Case-based Reasoning (CBR) is the most appropriate
methodology to build such a library, because it properly replicates the structure of an expert’s
mind. In fact, CBR is an effective problem-solving paradigm that involves matching the current
problem against problems that were solved successfully or unsuccessfully in the past (Watson &
Marir, 1994). In this case, the past problems will be the GECIs.
The origins of CBR are found in the work on cognitive sciences of Roger Shank and his
students (Shank, 1983; Shank & Abelson, 1977; Aamondt & Plaza, 1994). It began with a desire
to understand how people store information and how they use such information to tackle new
problems (Watson, 1999). The first emblematic definition of Case-Based Reasoning was given
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by Riesbeck and Shank in 1989 (Kolodner, 1992): “A case-based reasoner solves problems by
adapting solutions that were used to solve old problems.”
The first computer program that used CBR was developed by Janet Kolodner (1983). It
stored events of the lives of former secretaries of state and answered questions posed in English
concerning such information. During the following years many CBR software applications were
developed and applied for many different purposes (Aamodt & Plaza, 1994). By 1996, CBR was
already considered a matured subfield of Artificial Intelligence and most of its key principles
were already established (Leake, 1996).
Although CBR has been widely used for Artificial Intelligence, it is not a technology, but
rather a methodology that replicate experts thinking to solve problems (Watson, 1999). Casebased reasoning is a “cyclic and integrated process of solving problem, learning from this
experience, solving a new problem, etc.” (Aamodt & Plaza, 1994). At its most general level, it
consists of four phases, also known as the four “REs” (Aamodt & Plaza, 1994):
1. RETRIEVE the most similar cases to the new case
2. REUSE the solution suggested by the previous case
3. REVISE the solution of the new case
4. RETAIN the validated parts of the new solution for solving future problems
As illustrated by Figure 1, the cycle begins with a new case. Similar cases are retrieved from the
central library and are matched against the new case. The old cases are reused to solve the new
problem. Then, the new solution is revised, e.g., by being applied in the real world or evaluated
by a teacher (Aamodt & Plaza, 1994). Finally, useful and innovative parts of the new solution are
retained and stored in the central library. The reader can notice that the central part of such a
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cycle is a library that contains general knowledge in the form of previous cases. Such a library is
the most important feature of systems that employ CBR.
Historically, CBR has been divided in two classes: interpretive CBR and problem-solving
CBR. Although such classes shares the same cycle, they present some major differences (J. L.
Kolodner, 1992; Leake, 1996). Interpretive CBR aims to classify or judge new situations by
matching and contrasting them with cases that have already been stored. For instance, the
American legal system provides a perfect domain for the application of interpretive CBR,
because the definite way for interpreting law is based on previous cases (Ashley, 1987; Rissland,
1983; Kolodner, 1992). As a matter of fact, lawyers use interpretive CBR when they use cases to
justify their argument (Kolodner, 1992).
Problem-solving CBR uses prior cases to suggest solutions to new circumstances. The
similarities and differences between the new and the old cases are used to adapt the new old
solution to fit the new situations. Because such a model reflects so well the reasoning that
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experts typically use to solve problems, it has been successfully applied in many engineering
applications. For instance, Haque, Belecheanu, Barson, and Pawar (2000) used problem-solving
CBR to create a system that could provide decision-making support for project managers and
engineers of Thomson CSF Service Industrie (France) and General Domestic Appliances Ltd
(USA/UK) during the early phases of product development. In the aerospace industry, Lockheed
and British Airways have used problem-solving CBR to assist engineers to solve various
problems (Hennessy & Hinckle, 1992; Malgadi, 1994; Watson & Marir, 1994). Most recently,
Cobb and Agogino (2010) have used CBR to “synthesize new microelectromechanical systems
(MEMS) design topologies that meet or improve upon a designer’s specifications.”
Due to the problem-solving nature of global engineering and engineering in general,
problem-solving CBR is the most appropriate methodology to create the GEEL. Hence, the
following pages explore the details of the problem-solving CBR at the heart of this proposal.
The Four “REs” of the CBR cycle
The most important phase of the CBR cycle is the first stage: retrieve. Only if such a step
is well done the CBR cycle can it effectively help engineers solve their problems. In general this
part consists of two closely related sub-procedures (Kolodner, 1992):

Recall previous cases. This step aims to find cases that can potentially make relevant
prediction about the solution of the new problem. At this step, a list of suitable cases is
created.

Select the best subset. This second step aims to winnow down the set of cases retrieved
during the previous phase to a smaller group of candidates that are worthy of further
consideration. At this step, the retrieved cases are ranked in order of similarity.
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At the base of the problems related to such phases, there is what Kolodner (1992) has called the
indexing problem. In general, it refers to the procedure of giving pertinent labels, called indexes,
to cases; so that only useful cases to solve the new problem are retrieved (Kolodner, 1992). Thus,
it is very important to carefully choose the most appropriate indexes to assign to each case of the
library. In particular, Watson and Marir (1994) suggested that indices should:

be predictive

address the purposes the case will be used for

be abstract enough to allow for widening the future use of the case-based library, and

be concrete enough to be recognized in the future.
Once the indexes have been assigned, the researcher can formalize what routines to use
for recalling “good” cases and selecting the most appropriate. Although there are various
methodologies to perform such tasks, the similarity assessment approach is the most widely used
and simple, but not necessarily the most efficient (Lopez De Mantaras et al., 2006). First, the
new problem is characterized by assigning features to it. Features are very similar to indexes.
Then, the features of the new problem and the indexes of the stored cases are matched and
ranked. Once the most suitable cases are retrieved, the user can advance to the reuse phase. The
reuse phase consists of adapting the old cases to solve the new problem (Aamodt & Plaza, 1994;
Kolodner, 1992; Lopez De Mantaras et al., 2006; Watson & Marir, 1994). Such a task can be
completely automatic and executed by the computer, or it can be performed by the user, e.g., an
engineer (Kolodner, 1991).
After adapting the old solution to solve the new situation, the user will need to evaluate
the solution adapted. This task is the only routine that has to be completely performed outside the
CBR system (Aamodt & Plaza, 1994). The user tries the new solution in the real world and tests
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its effectiveness (Kolodner, 1992). If the solution is found to be successful, it is stored in the
case-based library. Otherwise, if it fails in the real world, it is repaired and then stored (Aamodt
& Plaza, 1994). The storing phase is known as the retain step and it involves again the indexing
problem. Hence, proper indexes have to be assigned to the new solution, so that it can be easily
retrieved in the future.
Case-based reasoning offers many advantages that make it very appealing for assisting
engineering to undertake new projects (Kolodner, 1992). In fact, users can:

find solutions quickly and effectively, and avoid starting from scratch,

solve problems in knowledge domains they are not familiar with,

interpret ill-defined and open-ended problems,

prevent making mistakes that were already made in the past, and

focus on the important part of the problems (Kolodner, 1992).
Moreover, CBR can be efficiently used:

to create a system that can help humans make the right decisions in contexts that they are
not acquainted with (Kolodner, 1991); and

as an instructional tool to train novices in specific domains (Jonassen & HernandezSerrano, 2002).
Thus, the proposed GEEL can be developed as a decision-aiding system that engineers can query
whenever they need, or as an instructional support to existing cross-cultural training. Finally,
many open source tools are available that implements features of the CBR methodology, deleting
the problem of programming a potentially complicated system (Watson, 2009).
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Project Activities and Timeline
To achieve the three objectives of this proposal, the project will take 10 months during
which three different deliverables will be handed to industrial partner. At the end of the project,
the industrial partner will possess the list of GEEs, instruction on how to elicit experience and
create GECIs, and a prototype of the GEEL containing the first set of GECIs. As a consequence,
once the project is over, the industrial partner will be able autonomously to continue populate the
library. Table 2 summarizes all major activities organized by month. The sections that follow
summarize all activities related to each phase.
Phase
Activities
Phase 1
Administer CQS
Months 1 to 3
Month 3 to 7
Month 7 to 10
Select top 6 GEEs
Phase 2
Interview GEEs
Create GECIs
Phase 3
Create GEEL prototype
Table 2. Timeline and Project Activities
Phase 1: Identification of Global Engineering Experts (GEEs)
In this phase the Cultural Intelligence Scale and the background information form will be
administered to as many engineers within the firms as possible. If at least 10 GEEs are found, it
means that the industrial partner possesses an adequate number of experts to build an extensive
library. Of the GEEs found 6 will be selected for the successive phase. The selection will be
based both on high scores of CQ and on the richness of international experiences. Such GEEs
will be contacted and meeting organized. If two or three GEEs work at the same location, focus
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group interview will be utilized, so that the researchers will have to undertake a single trip. If it is
not possible to meet in person the selected GEEs, meeting can be arranged by phone, or an
online form can be developed. At the end of this phase, a list of all the GEEs within the firm will
be delivered.
Phase 2: Creation of Global Engineering Critical Incidents (GECIs)
In this phase the researchers will meet with selected GEEs and interview them in order to elicit
and record their experiences. Moreover, during this phase, the experiences of GEEs will be
formalized as Global Engineering Critical Incidents (GECIs) that will function as the cases for
the Global Engineering Expertise Library (GEEL). Considering that each GEEs will share 1 to 3
valuable and unique stories, 6 to 18 GECIs will be created. In order to optimize time, collection
of experiences and creation of GECIs will be done in parallel. At the end of this phase, a
document containing GECIs and instruction for eliciting and creating GECIs will be delivered to
the industrial partner.
Phase3: Creation of the Global Engineering Expertise library (GEEL) Prototype
In this phase the searchers with the help of an undergraduate programmer will develop and test a
prototype of the GEEL. This initial version of the GEEL will contain the GECIs collected during
the previous phase. At the end of this phase, the GEEL prototype and instructions on how to
expand the GEEL will be delivered to the industrial partner.
Budget
The overall cost of the project will be of 48,687$. The budget of the project includes funding the
graduate student who will work at a rate of 10 hours per week over the 10 months necessary for
the accomplishment of the project. During the last 12 weeks of the third phase, an undergraduate
programmer will be hired to assist the graduate student to develop and test the GEEL. The PI
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will function as a consultant and paid accordingly. Finally, expenses include multiple travels to
interview GEEs and cost for communication and webhosting for the development of the GEEL.
Table 3 summarizes all the expenses.
Item
Description/Rate
Amount ($)
Graduate Researcher
(GRA)
10 months @ 10 hours/week
18,507
Faculty Consulting
20 days x $500/day
10,000
Undergraduate
programmer
12 weeks x 40 hours/week x $12/hour
6,295
Overhead
Other - travel
Other - web host
Other - telecomm
54.00% of select expenses
8,685
5 on-site trips x $1000/trip
10 months x $ 10/month
10 months x $10/month
5,000
100
100
Total
48,687
Table 3. Budget
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Personnel Profiles
Andrea Mazzurco – Student Investigator
PhD student at the School of Engineering Education, Pudue Univesity.
Research interests:
Global engineering education, human-centered design, humanitarian engineering, and servicelearning.
Educational backgroung
 Master of Science in Aeronautics and Astronauitics, Purdue University
 Bachelor of Science in Aerospace Engineering, Politecinico di Milano, Milano, Italia
Pubblication:
Mazzurco, A., Jesiek, B. K., Ramen, K. D. (2012). Are engineering students culturally
intelligent?: preliminary results form a multiple group study. ASEE conference and exposition
Contact
email: [email protected]
phone: 765-237-7305
Brent K. Jesiek – PI
Assistant professor at the School of Engineering Education and School of Electrical and
Computer Engineering, Purdue University
Associate director of the Global Engineering Program (GEP), Purdue University
Research Interests:
Historical and social studies of engineering, engineering education, and computing; global
engineering education
Contact
email: [email protected]
phone: 765- 496 -1531
website : http://web.ics.purdue.edu/~bjesiek/
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