PDM Field Study in Collaborative Engineering Education – Results from 2016/17

Abstract

This work describes the research activities performed in the field of engineering education at Austrian Federal Colleges of Engineering (HTL) in collaboration with both Graz and Vienna Universities of Technology. To support the collaboration process in engineering education, a Product Data Management (PDM) system was introduced to several Austrian HTLs. In the academic year 2016/17, a field study was begun and that will continue in 2017/18 to determine how to enhance collaboration between students by using this kind of software and methods. This paper, which is continuing a paper presented at ICL2017 in Budapest presents the first results, obtained in the academic year 2016/17 of the survey and the impression of the lecturers regarding the level of collaboration within students’ design projects.

Full text

Paper—PDM Field Study in Collaborative Engineering Education – Results from 2016/17
PDM Field Study in Collaborative Engineering
Education – Results from 2016/17
https://doi.org/10.3991/ijoe.v15i04.9753
Andreas Probst(*)
HTL Ried, Ried/Innkreis, Austria
Andreas.Probst@eduhi.at
Martin Ebner, Martin Schön
Graz University of Technology, Graz, Austria
Detlef Gerhard
TU Wien, Vienna, Austria
Abstract—This work describes the research activities performed regarding
engineering education at Austrian Federal Colleges of Engineering (HTL) in
collaboration with both Graz and Vienna Universities of Technology. To pro-
vide assistance to collaboration in engineering education, several Austrians
HTLs were introduced to a Product Data Management system (PDM). A field
study was conducted in the academic year 2016/17 that will continue in
2017/18 to determine how to utilize this type of software and methodology in
order to encourage and improve collaboration between students. This paper pre-
sents the initial results of the survey, obtained in the academic year 2016/17 and
the impression of the lecturers regarding the degree of teamwork exhibited by
students within their design projects.
Keywords—Design practice, Product development, Outcomes based assess-
ment, Higher education, Engineering Education, Teamwork, Engineering Col-
laboration, PDM
1 Introduction
Working in teams is a key skill for engineers because coping with complexity in
engineering projects demands the contribution of specialists in different engineering
disciplines, rather than the work of a single designer [1]. Martinec [2] stated that dur-
ing conceptualization within design project tasks like goal formulation, ideation and
decision-making teamwork are certainly important and need some effort put into
them. Therefore, collaboration in engineering education is common practice. Espe-
cially in mechanical engineering design education is performed with special tools
known as CAD systems, but for collaboration additional tools called Product Data
Management systems (PDM) systems are needed. While there are several papers [3],
[4], [5], [6] and books which describe the advantages and usage of PDM systems [7]
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there is no existing research about the progress in collaboration of students using
PDM tools. The motivation for the research presented in this paper is to obtain
knowledge about how to best utilize these types of software and methods to improve
inter-student collaboration.
2 Background and Related Work
This research work is the follow-up to the research study [9] presented at the Inter-
national Conference on Interactive Collaborative Learning ICL2017 in Budapest
Hungary.
2.1 Collaboration in engineering education
When looking at different methodologies for engineering education, Problem-
Based learning and Project-Based learning can be identified as the two major ap-
proaches within previous research.
Mills et al. [10, p. 8] determine in their paper the difference between Problem-
Based learning and Project-Based (PBL) learning in the field of engineering educa-
tion. They stated that “Project-Based learning is usually accompanied by subject
courses (eg maths, physics etc. in engineering), whereas problem-based learning is
not”. Furthermore, they identified Project-Based learning frequently used in K-12
education.
Looking at teaching, Kolmos [11] identifies different teaching roles. For Project-
Based learning the role of a “product-oriented supervisor” is compared to a “process-
oriented supervisor” in Problem-Based learning scenarios. Additionally, she identifies
a difference at the problem-solving level; that project work has more to do with both
problem analysis and problem solution than Problem-Based learning which focuses
mainly on problem analysis.
Searching for differences between the two approaches Perrenet et al. [12, p. 348]
find “Project-Based learning is more directed to the application of knowledge, where-
as Problem-Based learning is more directed to the acquisition of knowledge”.
It seems that the Project-Based learning approach is what is generally utilized in
Austrian HTLs, especially in labs and engineering design education lessons.
In their paper Johnson and Johnson [13, p. 2] identify five required conditions for
team management to get better results than competitive or individual performance”.
• Clearly perceived positive interdependence
• Considerable promotive (face-to-face) interaction
• Clearly perceived individual accountability and personal responsibility to achieve
the group’s goals
• Frequent use of the relevant interpersonal and small-group skills
• Frequent and regular group processing of current functioning to improve the
group’s future effectiveness”
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Considering this, students improve their teamwork skills, doing educational design
projects in groups after starting their third year out of five.
Oakley et al. [14] identify in their paper several basic conditions to turn student
groups into effective teams:
• Ensuring that expectations are reasonable by establishing clear guidelines
• Giving team members useful information on team practices that work well before
they begin working with one another
• Effectively handling problems with team members
Regarding the optimal team size, they state that there is no consensus in the litera-
ture, but most authors agree that the minimum team size for most team tasks is three
and the maximum is five. Two people may not provide enough ideas, skills, and ap-
proaches to maximize the outcome of the group work. In addition, conflicts among
pairs of people tend to be won by the more dominant partner, even if they are wrong.
However, if a team consists of six or more individuals, at least one tends to contribute
less than the others. In regard to this, the team size for the field study is determined to
be three, four or five team members, depending on the overall number of students
attending a class.
In their work Lu et al. [8] distinguishes between coordination, cooperation, and
collaboration. Considering his classification (see Table 1) (especially for small stu-
dent groups’) collaboration seems to be the most appropriate form of working on
common tasks and objectives. They also state that traditional engineering can be iden-
tified as a decision process with many technical aspects or “task work” conducted by
individuals, whereas collaborative engineering must also be thought of as a social
collaboration for the sake of teamwork and reaching agreement between those in-
volved
Table 1. “Collective human endeavor characteristics” [8, p. 615]
Stakeholder
Resource
Goal
Task Structure
Coordination
Large Community
Limited and
Exchanged
Multiple &
Competing
Pre-defined, same layer in
hierarchy, uni-direction
Cooperation
Mid-size Group
Limited a
nd
Shared
Multiple &
Private
Pre-defined, across layers
in hierarchy, bi-direction
Collaboration
Small Team
Limited, Shared
Complementary
Single &
Common
Undefined, non-
hierarchical, multi-
direction
Furthermore, Lu et al. see “collaborative engineering as the synergy between team-
work and task-work” [8, p. 620]. They observed the common characteristics of suc-
cessful teams within industry, including: [8, p. 610]
• It is best to make joint decisions since these lead to better outcomes than individual
decisions.
• Groups are able to be open minded and honest in theirs communications with each
other.
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• All team members have the ability to contribute their own creativity in both the
creation and claiming of value when making group decisions
Additionally Lu et al. [8, p. 610] declared “desirable results from a collaborative
engineering team should go beyond task-work agreements that meet stakeholders’
competing interests and requirements”. They also state that teams should avoid limit-
ing their decision-making process. Instead, they should allow themselves to think of
multiple alternative possibilities and solutions.
Lu et al. [8, p. 605] also stated “Although we can recognize the good results of
successful collaborations, our ability to re-create the desired collaboration process and
train engineers to better collaborate with each other is still very limited”
2.2 PDM in engineering and engineering education
Although PDM and PLM are commonly used in industry, Small and Medium En-
terprises (SME) seem to use such tools only when they are part of a supply pyramid.
In this case, the Original Equipment Manufacturer (OEM) and their supplier have to
develop together and share product data. Schuh et al. [6, p. 211] report in their publi-
cation that a survey “in the automotive industry have shown that there is a wide gap
between the current implementation status and the state of the art”. Though the re-
search mentioned in this research study was done several years ago, the situation has
not changed a lot. Duigou et al. [15] described in their paper framework for modeling
to support the use of SMEs as part of the implementation process of PLM systems.
Despite these facts, the authors of this paper believe that the single source of data is
very important for international operating teams, therefore working with PDM pro-
grams has to be trained in engineering education classes.
Mamo et al. [16] described a Global Design Exercise between three universities
carried out as a project involving collaborative design, in which, for eight weeks,
engineering students from various disciplines and cultural backgrounds participated.
They investigated “the patterns in the use of design tools by students in engineering
design to collaborate with each other”.
One outcome of the project was the information about students’ usage of online
tools like Skype, Dropbox, Whatsapp, and Facebook together with professional de-
sign tools like CAD to solve a given design task. In his research work Barrie [17, p. 1]
reports about this Global Design Exercise that the “CAD modelling process was con-
ducted by a single person with inputs from other team members, mostly via Facebook
and Skype”. This lack of usage of design tools seems to show an urgent need for web-
based PDM tools, case of use (usability) and a simple user interface.
Although there have been several efforts to introduce PDM systems and methodol-
ogies like concurrent engineering design and development at Austrian HTLs [18] [19]
[9], the acceptance among students and staff has been mixed. The benefits for stu-
dents (like access to data from any place and collaboration with other students) and
the benefits for staff (like the possibility of getting information about workload over
time, information about the students who have genuinely worked on the project data
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as well as the possibility to administrate projects and access) seem to be opposed by
the effort needed to learn an additional program within students education.
3 Research Questions
Regarding research work three research questions are specified:
RQ1: Can collaboration within the student groups be easily identified and moni-
tored?
RQ2: What challenges occurred over the course of the field study?
RQ3: How many of the tasks were completed during the allotted time?
Concerning the first research question the different file versions displayed in Fig-
ure 1 of each compulsory file from Figure 2 are collected in a database. The question
is how often students alternately edit and store the different files given within their
task. Evaluating all of the data from each student group will provide information
about how students collaborate and how they utilized the PDM system.
Fig. 1. Different file versions created by two students within PDM database.
The second research question is about difficulties occurring during the field study
which could not be identified from looking into the PDM database. For this reason,
student notes in Table 9 and teacher’s observations and notes are evaluated in order to
obtain extra information to be used in the field study.
The third research question regarding the number of completed tasks within the
given timeframe will, when combined with the evaluated PDM data, determine if a
correlation between the collaboration of the students and the percentage of tasks com-
pleted exists.
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4 Research Methodology of PDM Field Study
4.1 PDM field study set-up
The field study was intended to be conducted within 10 Austrian Federal Colleges
of Engineering (HTL) [9] but due to daily business reasons only three HTLs partici-
pated in the study in the academic year 2016/17. In the academic year 2017/18, the
field study is being continued amongst several other HTLs. For the purposes of the
field study, students in the participating classes are split into groups of three or four
individuals and must solve the given assignment together using the CAD program in
conjunction with the PDM software. The students have a maximum of four hours to
complete their tasks and receive no instructions on how they should work together.
Design task and deliverables: The design task is a single stage gear transmission
(see Figure 2) where some parts like the gearbox housing already exist and are stored
within the PDM database. The figure also shows the deliverables for the PDM field
study, however stress calculation or any other calculation of any part is not included
in the study. Despite teachers not telling them to do so, students split up the work they
are assigned to do between themselves and decide how they will collaborate.
Fig. 2. Design task single stage transmission 2D drawing [20] and 3D CAD model
Instead of giving grades to the student projects, as we first intended [9], the per-
centage of completion within the task regarding the following aspects is evaluated:
• Parts displayed in the drawings are producible
• Completed dimensions of the parts
• The necessary surface symbols and tolerances are displayed on the drawings
Parameter used for collecting data: For each group the following data are col-
lected.
Pinion shaft
Gear Wheel
Output shaft
Bearing cover
Gear assembly
Pinion shaft Gear Wheel
Output shaft
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Table 2. Parameter used for calculation
No.
Parameter description
Sub parameter
1
Austrian Federal Colleges of Engineering
(HTL)
2
Group Nr.
3
Students / group
4
Female students / group
5
Percentage of collaboration
6
experience w. PDM [months]
7
Percentage of completion
8
Saved file versions / group
9
Improvements within data conflicts
Number of occurrences [quantity]
10
Improvements within data conflicts
Time for correction [minutes]
11
3D interface problems
Number of occurrences [quantity]
12
3D interface problems
Time for correction [minutes]
13
Coordination concerning 2d working draft
Number of discussions [quantity]
14
Coordination concerning 2d working draft
Time for each discussion [minutes]
15
Working on the same parts or assemblies
Number of occurrences [quantity]
16
Working on the same parts or assemblies
Time for correction [minutes]
17
Duration of field study
18
Time field study started
19
Semester of field test
1=winter semester /
2=summer semester
20
HTL year
PDM field study data: In Table 3 the collected data from the field study so far are
displayed. Data were collected from three different HTLs among four classes with a
total of 18 groups consisting of three or four students each. The overall number of
students was 68 which included three female students; the subjects of these classes are
Mechanical Engineering (MB) or Mechatronics (ME).
Table 3. Overview involved HTL classes PDM field study
Colleges / HTL
Graz
Wien3
Wien3
Ried
Total / remarks
Subject
MB
ME
ME
MB
HTL year
3rd
4th
4th
5th
Students’ average age
16 years
17 years
17 years
18 years
Experience w. PDM
3
2
2
1
[months]
Groups / class
4
7
5
2
18
Students / group
4
4 / 3
3
4
Students total
16
26
18
8
68
Female students
3
0
0
0
3
Evaluation time
3:05
3:25
3:25
3:15
[hh:min]
Evaluation of collaboration in field study: In the academic year 2016/17, three
Austrian HTLs with 18 groups and a total of 68 students participated in the field
study. Each HTL class was split into groups of three or four individual students, with
each group being giving a single PDM project to work on. The CAD and PDM data
obtained from each student group stored within the PDM system were then collected
and all of the data were collectively analyzed anonymous through a process of pair-
reviewing. Table 4 portrays one such evaluated example of a PDM project from a
four-student group. For example, the fifth row indicates that the students managed to
create the deliverable of the 3D-CAD model of the output shaft and thus completed
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the task entirely. In the table, “A” represents student A, “B” student B etc., therefore
one can see that only student D worked with this file and stored it in two versions.
Table 4. Evaluation saved file versions from PDM database
HTL-nnn-Group-x
Task %
finished
File version
Deliverable
File type
1
2
3
4
5
6
7
8
9
Gear assembly
assembly
100
B
B
B
B
B
B
B
B
drawing
100
B
B
B
B
Output shaft
3D part
100
D
D
drawing
100
D
To evaluate the collaboration for each deliverable (see Table 4) we determine
which file version was created by which student and an expectancy value is calculated
by dividing the total file versions by the number of students. Afterwards a Chi² func-
tion is calculated for each student’s task as well as the sum of all Chi² Values. Lastly,
the value for the p – significance is calculated. An example of collected and calculat-
ed data for the 3D CAD part of the pinion shaft from Table 4 is shown in Table 5.
Table 5. Evaluation example of students’ collaboration
File versions
/Student
Total
versions
Expectancy
value
Chi² / Student
Total
File versions
/Student
1
2
3
4
1
2
3
4
7
0
0
2
9
2,25
10
2,3
2,3
0
14,6
0,0007
A high Chi² value means a deviation of uniform distribution as well as a low p-
significance value, and a low collaboration between students for editing and saving
data from the given field test task.
4.2 Evaluation of occurring difficulties in field study
Over the course of the study students were required to take written notes of certain
effects which are then evaluated, as seen in Table 6. This gives the opportunity to get
information in addition to that gained from the PDM database.
Table 6. Overview pattern of students’ notes for PDM field study
Measurable effect
No.
Collected data
Remarks
Improvements within
data conflicts
9
Number of occurrences [quantity]
10
Time for correction [minutes]
3D interface problems
11
Number of occurrences [quantity]
3D parts and assembly
do not match
12
Time for correction [minutes]
Coordination concern-
ing 2d working draft
13
Number of discussions [quantity]
Students have to carry
out and adapt a work-
ing draft
14
Time for each discussion [minutes]
Working on the same
parts or assemblies
15
Number of occurrences [quantity]
Due to a PDM concept
only one person can
work on one part at
one time
16
Time for correction [minutes]
Individual students
notes
Effects and remarks
individual evaluation
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The quantity and minutes are collected from every student’s individual notes but
are only processed for a whole group. Teachers were also required to make note of
several effects such as the start and end time of the field study as well as individuals’
notes like problems with students’ computers or students leaving the field study early.
This will give more accurate information about whether groups are performing effi-
ciently or not.
4.3 Evaluation of PDM field study completed tasks
The evaluation of the percentage of finished tasks (see Table 4) is done anony-
mously by two teachers according to given criteria that students and teacher know
from the beginning of the test. These criteria are:
• Assemblies are complete
• No interface problems like intersections between parts are occurring
• Parts are producible
• Dimensioning of the parts in the drawings is complete
• Expedient surface symbols and tolerances are on the drawings
• No geometrical dimensions and tolerances are needed in the drawings
• Bill of material is present and completed (e.g. material, good part names, …)
5 PDM Field Study Results
5.1 Results for collaboration in field study
A heuristic method of a principal component analysis is used to present the data
matrix more simply and manageably, see Table 7. The first four principal components
(Table 7, rows 1 to 4) describe 84.55 percent of the total variance. In order to interpret
these components, the correlations of the original variables with these most important
components 1 to 4 are considered.
For the interpretation, the Variamax rotation method (see Table 8.) has proven to
clearly allocate the variables to one of the four factors. Values below 0.25 are not
shown in the rotated component matrix.
Since we are particularly interested in the degree of collaboration, the columns
with components 3 and 4 in Table 8 are especially interesting since the parameter
grade of collaboration only occurs there. In column 4, a higher degree of collaboration
is associated with group size and duration of the experience with PDM programs.
Since the group size varies only between groups consisting of 3 or 4 group members,
this means that the groups of four seem to have a higher degree of collaboration than
the groups of three.
As a matter of course, an odd group size is recommended for a board of manage-
ment because it enables quicker decision making. In the case of a vote, a majority will
inevitably arise. But what about cooperation?
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Table 7. Principal component analysis based on parameters and collected data
Principal
component
Sum
Variance
[%]
Accumulated
[%]
1
6,434
40,211
40,211
2
3,851
24,071
64,282
3
1,971
12,32
76,602
4
1,272
7,953
84,555
5
0,776
4,852
89,407
6
0,697
4,356
93,763
7
0,514
3,211
96,974
8
0,219
1,372
98,346
9
0,111
0,693
99,039
10
0,092
0,578
99,617
11
0,044
0,275
99,892
12
0,016
0,101
99,993
13
0,001
0,005
99,999
14
0
0,001
100
15
4,52E-05
0
100
16
-1,41E-15
-8,83E-15
100
Table 8. Variamax rotation matrix
Component
1
2
3
4
Students / group
-0,359
0,634
Female students / group
0,513
0,499
Percentage of collaboration
0,526
0,581
Experience w. PDM [months]
0,977
Percentage of completion
0,911
Saved file versions / group
0,889
Improvements w. data conflicts -Number of occurrences
[quantity]
0,536
-0,454
0,496
Improvements w. data conflicts - Time for correction
[minutes]
0,757
-0,47
0,345
3D interface problems - Number of occurrences [quantity]
0,901
0,381
3D interface problems - Time for correction [minutes]
0,941
Coordination concerning 2d working draft - Number of
discussions [quantity]
0,945
Coordination concerning 2d working draft - Time for each
discussion [minutes]
0,956
Working on the same parts or assemblies - Number of occur-
rences [quantity]
0,494
0,777
Working on the same parts or assemblies - Time for correc-
tion [minutes]
0,793
-0,455
0,359
Duration of field study
-0,608
-0,501
-0,486
Semester
-0,977
In an older study [21] 31 studies were reviewed with the conclusions: „group size
is an important variable which should be taken into account in any theory of group
behavior, and future research on group size should proceed more systematically than
in the past. There is no specified group size for most effective task performance.”
Intuitive theories regarding even numbers, odd numbers, and groups find that even
numbered groups are more harmonious. Menon and William Phillips [22] found „that
people view even numbers more favorably than odd numbers and predict that even-
sized groups are more peaceful than odd-sized groups. However, Study 2 found that
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three- and four-person groups without conflict did not differ, but three-person groups
with coalitions (two vs. one) produced more positive relationships than four-person
groups with coalitions (both two vs. two and three vs. one).”
On the site "How to Design Small Decision Making Groups"
(http://www.intuitor.com/statistics/SmallGroups.html) we find „Rules for Optimizing
Small Groups“ and the recommendation „Groups should have an odd number of
members. This prevents ties and improves the odds of making a correct decision when
using majority rules.”
We see: There is no specified group size for effective task performance. Obviously,
it depends on the situation. According to our data, groups with four members work
together more efficiently than those with three. However, the data base is still very
thin.
For the next field study in the academic year 2017/18 we plan to divide the stu-
dents into equal numbers of groups of three and four.
5.2 Results regarding difficulties in field study
There are groups without any values and groups with extremely high values. The
first groups (Graz groups 1 to 4, Wien3 groups 1 to 6 and Ried groups 1 and 2) had
reported problems using PDM functionality. Meanwhile, it is possible that the HTL
Wien3 groups 7 to 15 did not have enough time to make notes. In particular, group 4
of HTL Wien3 had reported 22 issues connected with data conflicts (Table 9, parame-
ters 9 and 10), which took 300 minutes of work on the problems connected with
PDM. Additionally, they reported 23 occurrences connected with 3D interface prob-
lems (Table 9, parameters 11 and 12) which took 360 minutes to work on a solution.
Table 9. Difficulties measured during field test over all groups
Parameters of Table 2
HTL
Grp.
P9
P10
P11
P12
P13
P14
P15
Graz
1
2
4
8
31
12
100
6
Graz
2
8
80
7
47
Graz
3
13
115
8
82
6
34
12
Graz
4
12
20
9
46
4
12
5
Wien3
1
30
210
6
Wien3
3
6
60
8
Wien3
4
22
300
23
360
31
260
8
Wien3
5
1
3
Wien3
6
12
80
1
Wien3
7
Wien3
8
Wien3
11
Wien3
12
Wien3
13
Wien3
14
Wien3
15
Ried
1
1
60
4
40
4
Ried
2
4
30
6
45
5
Average
5,00
44,39
3,17
33,44
4,56
34,33
3,06
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Looking into the evaluated data, while being a group of three students with 2
months experience in PDM, only two students worked on the project, whereas the
percentage of finished tasks was with 19,6% significantly below the average of
62,58% for all groups (see Table 10). Looking into the individual students’ notes, it
seems the students especially had problems working with the PDM system (name
conflicts, check-in CAD-files, check-out CAD-files, synchronizing the workspaces
both local and on the server), whereas using CAD was no issue. In contrast, the stu-
dents of HTL Ried, who were in their last year at HTL and had one month of experi-
ence with PDM (the lowest value in the field study), had significantly fewer problems
using the PDM system.
5.3 Results of PDM field study completed tasks
Table 10. Evaluation of finished tasks for all groups
% finished
assembly
3D Gear assembly
67,17
drawing
2D Gear assembly
34,72
3D part
3D Output shaft
87,72
drawing
2D Output shaft
34,94
3D part
3D Gear Wheel
99,17
drawing
2D Gear Wheel
22,44
3D part
3D Pinion Shaft
90,00
drawing
2D Pinion Shaft
46,39
3D part
3D Bearing Cover 1
84,44
drawing
2D Bearing Cover 1
56,39
3D part
3D Bearing Cover 2
80,28
drawing
2D Bearing Cover 2
35,56
3D part
3D Bearing Cover 3
70,56
3D part
3D Bearing Cover 4
66,39
Average
62,58
Table 10 and Figure 3 give an overview about completion rates of each task for all
groups as well as the average of completion at 62,58%.
Comparing the percentages of task completion it can be identified that 3D parts
and assemblies over all projects done are completed at an average high level of
80,72% whereas the 2D drawings are completed with an average of 38,41%. Overall
the tasks are completed with an average of 62,58%, which is less than can be ob-
served in common education design projects and may be an impact of stress during
the field study.
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Paper—PDM Field Study in Collaborative Engineering Education – Results from 2016/17
Fig. 3. Comparison of finished 3D and 2D field study tasks
5.4 Results for research questions
Results RQ1: Is collaboration identifiable within the student groups?
Concerning RQ1 collaboration can be observed, although the correlation with group
size needs additional field tests and evaluation.
Results RQ2: What difficulties occurred during the field study?
It can be observed that some student groups had problems using the PDM software,
whereas other student groups left no information to evaluate. Due to these problems,
there seems to be a growing need for user-friendly and simple-to-use interfaces [23].
Results RQ3: How many tasks were completed in the given time?
Looking at RQ3, it can be observed that the 3D parts and assemblies are completed at
a higher level compared to the 2D drawings. This is unsurprising to the authors, since
generating drawings takes more time due to the need to consider several engineering
standards.
6 Conclusion and Further Work
Collaborative engineering is an increasing part of daily engineering work, which
will get more important in the near future, due to worldwide linked development and
production in industry. Therefore, introducing and using PDM programs to support
students work on collaborative engineering seems to be a good way to prepare stu-
dents for the requirements of the labor market.
The PDM Field Study tests are ongoing in the current academic year 2017/18 to
complete the data set obtained so far. There will be a focus especially on the second
research question about difficulties occurring during the tests, to figure out and if
possible mitigate the students’ problems with PDM programs.
Despite the few successes that have been achieved so far, it will take additional ef-
forts to introduce PDM methodology into engineering education. Starting with intro-
ducing PDM into mechanical engineering design education, it will be necessary to
introduce it to electrical and mechatronics engineering as well. Due to this, students
will learn how to develop products within collaborative engineering, which will be a
key success factor in a changing industrial environment.
0,00
20,00
40,00
60,00
80,00
100,00
3D % finished
0,00
20,00
40,00
60,00
80,00
100,00
2D % finished
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2_53
8 Authors
Andreas Probst is with the HTL Ried a Technical Secondary College of Engineer-
ing, Ried, 4910 Ried / Innkreis, Austria
Martin Ebner is with the Educational Technology, TU Graz - Graz University of
Technology, 8010 Graz, Austria
Martin Schön is with the Educational Technology, TU Graz - Graz University of
Technology, 8010 Graz, Austria
Detlef Gerhard is with the Mechanical Engineering Informatics and Virtual Prod-
uct Development, TU Wien - Vienna University of Technology, 1040 Wien, Austria
Article submitted 31 October 2018. Resubmitted 29 November 2018. Final acceptance 03 December
2018. Final version published as submitted by the authors.
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