Model Based Study of Higher Education of Engineers
Ying Qian Sha Zhu Hans-Rolf Vetter Bo Hu
Shanghai University Shanghai University Universitat der Universitat der
Bundeswehr Munchen Bundeswehr Miinchen
CN-200444 Shanghai, CN-200444 Shanghai, D-85577 Neubiberg, D-85577 Neubiberg,
hina China Germany Germany
iris_qian@hotmail.com 1173366590@qq.com prof.vetter@gmx.de bo.hu@unibw.de
Abstract
In this paper we present our approach combining empirical quantitative questionnaires and
qualitative interviews with system dynamics modeling and simulation. Our preliminary
researches show that to improve the attractiveness of engineering studies at a university various
efforts may be taken such as the improvement of the success rate of engineers within the related
industry sector, a higher quality of practice-oriented teaching and more cooperation between
universities and companies. In contrast, an expansion of enrollment of students will lead to an
opposite effect.
1. Introduction
To improve the attractiveness of engineering studies for young people has significant importance,
especially in a country like China where manufacturing has been and will be a major industry in
its economy. Despite the expansion of high education in China the industry experiences
difficulties finding good engineers. The companies have to take great effort in training engineers
by themselves. There is a gap between engineering education and engineering practice.
Researchers both from educational background and engineering professions have attempted to
change this situation [22, 18, 17]. The results, however, are not satisfactory [27]. The apparent
gaps still exist and even become bigger in recent years. The higher education expansion in China
directly results in the amount of engineering graduates significantly surpassing the demand of
labor markets [34]. The job market for Chinese engineering graduates is now more severe than
before. Many students have to work in unrelated areas.
Generally, researchers attribute this gap to ineffective teaching methods such as insufficient
practical experience [18], lack of communication skills [2], insufficient capability of transferring
knowledge in new environments [20]. There is not only a competence gap [15], but also a
demand gap. One the one hand, the constrained education resources as well as the limited
practical collaboration projects with industries cannot equip engineering graduates with sufficient
competitive skills. On the other hand, the oversupply situation even hampers the careers of
engineering graduates and forces them to work in engineering-unrelated positions. It’s imperative
to provide feasible solutions so as to bridge the gap in China.
A couple of Chinese researchers discussed this problem from a theoretical perspective [26, 31].
Only few of them investigated the fundaments of this problem or provided solutions based on
quantitative analysis.
In this paper we present our approach combining empirical quantitative questionnaires and
qualitative interviews with system dynamics modeling and simulation. Our preliminary
researches show that to improve the attractiveness of engineering studies at a university various
efforts may be taken such as the improvement of the success rate of engineers within the related
industry sector, a higher quality of practice-oriented teaching and more cooperation between
universities and companies. In contrast, an expansion of enrollment of students will lead to an
opposite effect.
In the following Section 2 we introduce some related works. Section 3 describes our recent
research study at the Shanghai University. We present and discuss our preliminary results in
Section 4. Section 5 concludes this paper.
2. Background
Higher education is an interesting field for the application of system dynamics research. A
taxonomy of system dynamics models of educational policy issues has been presented by [5].
From a general point of view, a university model contains four sectors: students, quality, faculty
and facility [36].
Students as paying customers of universities justify a closer investigation. Applications,
admissions, enrolled students, drop-outs, graduated students, reputation of the university, and the
available budget for the students are the stocks of the system dynamics model presented in [16].
Funding and capacity planning, students sector, research and publications may additionally be in
focus [21] as well as curriculum in development and in use, faculty, tenure track and tenured
faculty [6].
Many researchers have also paid attention to the interaction of the university with the world
outside. For example, a system dynamics model which targets increasing the number of students
both capable and interested in pursuing careers in science, technology, engineering, and
mathematics has been presented by [24]. Both economic benefits and prestige factor for desiring
enrollments were discussed in [23]. The student sub-model may include research output by
students while the growth of project clients and innovative companies may also be taken into
account [25]. On the other side, unemployment due to time delay because of changing markets
has been addressed in [4]. Not only engineering students’ admission and graduation, but also the
post graduates’ employment status was modeled and simulated in [19].
In 2011 China established a “plan for educating and training outstanding engineers (PETOE)”.
An overview about the reform focus and innovation model of PETOE has been given in [11],
along with certain specific requirements on training standards, reform of curriculum systems,
enterprise training program, and internationalization. It has often been criticized that the Chinese
higher education for engineers today puts too much focus on the teaching of basic knowledge and
neglects practice oriented training. Too few teachers are facing too many students, and too few
internship positions are available [13]. Establishing problem/project based learning (PBL) [9] or
dedicating the last course year to practice (“3+1”) [35, 37] are thus among the suggestions to
build a more practice oriented curriculum system [10] for the implementation of PETOE. An
engineering education accreditation (EEA) has been requested [12].
One critical success factor of PETOE is constructing a teaching staff which is competent for this
task [7, 33]. Intensive cooperation between the universities and the enterprises on the one side,
the establishing of a teaching staff with both academic and industrial background on the other
side have been seen mandatory in [13, 32]. It has been pointed out that it takes a period of eight
to ten years to become a qualified engineer so that the higher education can only be a part of the
entire program [14]. It has to be remembered: As well as depending on academic education and
training the later process of successful professionalization relies to an extremely high degree on
the long-term biographical interests and attitudes as a whole [28]. PETOE should be implemented
in cooperation by universities and enterprises [1] or divided into the university working program
and the professional training programs [8].
3. Model supported study of higher education of engineers
Our current model supported study of higher education of engineers starts with the building of a
causal loop diagram which is based on a professional biographic concept [29]. We are interested
in the individually preceived reafference structure in which the past (the origin and the pathway)
leads biographically via the study in the presence to the future (professional development) [30].
3.1 A causal loop diagram
The first step of our model based study of higher education of engineers is to sketch a causal loop
diagram. As shown in Figure | our causal loop diagram contains two reinforcing feedback loops:
1. When the students experience a more practice oriented education, they will have a better
onboarding process. This will lead to a higher starting salary which generates motivation
for younger students to enroll in practice oriented learning so that a even better practice
oriented education will take place.
2. On the other side, when the students have a more practice oriented education, they can be
more successful in their work and the industries are more willing to cooperate with
universities. That will generate even higher motivation and capability for practice oriented
teaching so that students can have an even more practice oriented education.
The two reinforcing loops are a double-edge sword. It could work surely to generate more
practice orientation of education and better equip engineering students for their work. However, it
is also possible to trap everything on the low level, which means little practice orientation of
education, no cooperation, no motivation for practice orientation of education.
3.2 Quantitative survey and qualitative interviews
The aim of the survey is to understand the real learning activities of students, their study goals
and professional aspirations, as well as their subsequent placement in the labor market in the
context of their personal history, their canon of values and their experiences.
The biographical questionnaire contains 75 questions, organized mainly in seven sections:
¢ Statistical personal data
« General attitudes, comments, sense of self, self-awareness
¢ Educational trajectories of university and their attractions
¢ Rising ups of competencies, professional cognition structure, skills
¢ Career plan, future and hopes
¢ Ecological conditions of urban living
¢ Social embedding (at present, in the past, in the future)
motivation
attractive average
stal
GS enrollment é
expansion of
universities
motivation and capability successful
for practice oriented onboarding
teaching process
practice
orientation of
education
)
industrial
cooperations success in
knowledge work
Figure 1: A causal loop diagram as a starting point of the study of higher education of engineers
The questionnaire is complemented by several semi-structured biographical depth interviews [3].
The interview guide used has six sections:
e Decision for an engineer study
e The current situation at Shanghai University
e Family
e Gender
e Values
« Suggestions
Regarding the causal loop diagram in Section 3.1 both the quantitative survey and the qualitative
interviews are focused on the upper feedback loop (Figure 1): if and how the students are
motivated for a practice oriented study; if they expect a successful start into their professional
career and if they expect an attractive starting salary.
4. Preliminary results and discussion
4.1 Preliminary results
From Sep. 8th to 18th, 2015, 94 engineering students in Shanghai University took part in the
online survey. 43 ( 45.7%) were female; 19 (20.2%) were 18-22 years old (undergraduate
students), 71 (75.5%) were 23-27 (graduate students) and 4 or 4.3% were over 27 years old.
Table 1 shows all questions of which the answers were statistically “negative”. It is conspicuous
that 60 (63.8%) students didn’t choose an engineering major as their first wish.
Table 1: Answers to selected questions
1: Strongly agree, 5: Strongly disagree
‘12a eae [LANG
My university is high ranked 3 [2%] 56/9 | 1 [279
My major at my university is highly competitive 7 [35 [41 [| 0 [260
‘Are you convinced that the studies at this university will support you above-average compared
to other Universities and their offers? 1 | 30) 50) 12) 1 | 281
‘An engineers job is associated with high social benefits 4 | 37 | 40 | 73 [ 0 | 266
‘An engineer's job has attractive working time regulations 1 | 28 [40 [| 21[ 4 [299
‘An engineer's job has fair chance of 6 | 38 | 42 | 8 | 0 | 255
‘An engineer's job has good working conditions 4 | 28 | 36 5 | 2.96
‘An engineer has a higher social status and income compared to other professions 9 | 34 [38 [ 12 | 1 | 260
The housing situation in Shanghai is good 10 | 33 | 44 1 [255
My home town is not backward in comparison with Shanghai é[3 [9 [3 | 43 [41
‘Are the conditions of working and living actually more easy for you compared tothe generation] 4, | 31 | o7 | a6 | 5 | 267
of your parents?
Twill always feel higher pressure in the future and receive less support by my organization, my
family, my friends and my lover
1 44 4 12 3 2.60
Td rather be a technical leader than in a managerial leading position 9 | 33 | 36 [ 16 | o | 263
Twill be highly mobile in my career 2 | a7 [43 [30 | 2 | 314
Yes No
My major was my first wish Ey 60
It is noticeable that 56 engineering students (59.6%) answered that they still do not know if they
want to become an engineer. Table 2 shows nine correlations which can be classified as
statistically significant. The participants who do know that they want to become an engineer seem
statistically to have a stronger support by their parents, a more developed home town, and
altogether a more positive and proactive attitude to engineering as a profession, to their education
program by the university and towards the future.
Table 2: Decision to become an engineer and its correlations to some opinions
4: Strongly agree
5: Strongly disagree
Atthe age | Not yet,
6-18, N=38 |__N=56
aig. | stdv. | avg. | stv. [tvalue] dif [P-value|
‘After completing my studies at my university | will continue to improve my professional oui ora | ate lees | ata [tore ieee
knowledge as much as possible
When did you first think of becoming an engineer?
My parents encouraged me to study engineerin 1,023 |
Tid rather be a technical leader than in a managerial leading position 34
‘An engineers job is relatively stable 1 E 34
Tam interested in engineering E 54 M4!
‘Will you still be happy in ten years with your choice of becoming an engineer? 4 : 7
‘Are the conditions of working and living actually more easy for you compared to the generation! » 4- | 456 | > a9 14.01| 1.60 | 0.37 lo.t13
of your parents?
My home town is not backward in comparison with Shanghai 3.89 | 7,09 | 4.26 [7.73 | 182 | 0.36 | 0.132
‘Are you convinced that the studies at this university will support you above-average compared | > 49 | 9 79 | 65 |0.78| 1.60 | 0.24 [0.136
to other Universities and their offers?
Based on the data we obtained from the survey, we did in-depth interviews with five engineering
students at Shanghai University. All five students are master students coming from various
universities, where they did their undergraduate studies. They all reported very little practical
training during their undergraduate studies and couldn’t perform the most fundamental tasks
required for engineers. The internship requirements for undergraduate students had been just a
short visit to some company. There was no real hands-on experience involved, as the companies
and universities all had safety concerns. About half of their fellow students ended up not working
as engineers after graduating from university. Those graduates who find engineering jobs have to
learn how to do practical work when onboard and have to pass certain assessments set by the
engineering authorities in China.
4.2 Stock and flow diagram
Based on the causal loop diagram in Section 3.1 and the quantitative questionnaire described in
Sections 3.2 and 4.1 we develop our stock-and-flow model starting with the naming of involved
state variables. As shown in Figure 2, five stocks in the first row — Motivated, Studying,
Graduated, Employed in related areas and Successful — depict a possible
successful career of an engineer while the other two stocks — Quality and Capacity for
practice oriented teaching — are used to describe the state of a university providing
service and support for the development of such a career.
Figure 2: Seven stocks depicting a possible career of an engineer and the state of a teaching
institution
As shown in Figure 3, to motivate young people for an engineer career is a key responsibility
of a teaching institution. In the specific context of China, due to the college entrance examination
system Admission score relative can be seen as a KPI of a specific university major.
i
Motivated =|
a fo /
A
sco
Figure 3: Admission score as a key indicator
When students have completed their four-year study at university they become Graduated. Those
who find a job related to their major become Employed in related area, and
Starting salary relative is a key indicator in this part of the model. Those who
haven’t found a job in a related area is an outflow from graduated, change their career and
leave the chain of a successful development of engineering (Figure 4).
a
Motivated
Studying
Enroll
cata
sean Srpanion Emotent SUBD
‘ald profile: wai
Employed in a
(slated area [ me |
Watt time
relative
Motivation at
the beginning
Motivated at the
beginning
Figure 4: Starting salary relative as a key indicator
Over the time, as newly employed engineering graduates accumulate knowledge and skill in their
work, some of them become Successful (high salary and good social status). The
Successful relative is a key indicator for this part of the model (Figure 5).
& _ —— aa ee
Studying atthe — ;
ee Graduated athe “Staring stay <j Erpioymont at eee
| Es [ee
LN
Studying Graduated Succossful
Flastioity
volative
o ‘le Ma i He 4 i T a
wn quae Se TD ede
Motivation at
the beginning
Motivated at the
beginning
Figure 5: Successful relative as a key indicator
The quality of the engineering education is a key factor affecting the employment of the
graduated students. If the students are well educated, the starting salary will be high, and that will
attract more motivated students to start an engineering major. The quality of the engineering
education also directly appeals to more motivated students. Over the years, when graduated
students become successful engineers in the industry, it is easier for a university to Cooperate
with companies, which in turn will offer opportunities for undergraduate students to get practical
experience. And this is an important aspect of the quality of engineering education in China.
sstul at
ainning
eee
& St hi a eas ‘Successful
Lil now Loco Na == 2
4
é rie y
hange Quit save
Employment}
a,’
Motivation at
the beginning
Motivated at the
beginning
Figure 6: Closing the loops
Above, we have explained the model structure. The setting of parameters and equations are listed
in following tables.
Table 3: Stocks (Endogenous)
Variable Equation (Description) Dimension
Motivated = INTEG (Motivate-Demotivate-Enroll, Motivated at the beginning) Person
Studying = INTEG (Enroll-Graduate, Studying at the beginning) Person
Graduated = INTEG (Graduate-Employ-Change, Graduated at the beginning) Person
Employed in related = INTEG (Employ-Develop-Quit, Employed at the beginning) Person
area
Successful = INTEG (Develop-Leave, Successful at the beginning) Person
Capacity for practice = INTEG (Cooperate-Decline, Capacity at the beginning) Dmni
oriented teaching
Quality = INTEG (Improve, Quality at the beginning) Dmni
Table 4: Flows (Endogenous)
Variable Equation (Description) Dimension
Motivate = Motivation at the beginning*Quality*Starting salary relative Person/Year
Demotivate = max(0,Motivated/Wait time) Person/Year
Enroll = base *(1+Exp: Expansion profile(Time))) Person/Year
Graduate = DELAY FIXED(Enroll, 5 , Enrollment base) Person/Year
Change = Graduated/Stay time 0 Person/Year
Employ = ticity(Quality) *(1 jion(Time)) Person/Year
*Employment at the start)
Quit = Employed in related area/Stay time 1 Person/Year
Develop = Employed in related area*Factor 2 Person/Year
Leave = Successful/Stay time 2 Person/Year
Cooperate = Successful*Factor 3 Dmnl/Year
Decline = Capacity for practice oriented teaching/Effect time Dmni/Year
Improve = Capacity for practice oriented teaching*5/Studying*Admission score relative-1_| Dmnl/Year
Table 5: Auxiliaries (Endogenous)
Variable Equation (Description) Dimension
Admission score relative | = Motivated’ at the Lookup = Dmni
(0.165,0.702), (1,1), (4.65,1.35),(9.57,1.49)
Starting salary relative | = Employ*Graduated at the beginning/Employment at the Dmni
start/Graduated*Successful relative; Lookup = (0.5,0.5),(1,1),(5,5)
relative = at the beginning Dmni
Table 6: Constants (Exogenous)
Variable Equation (Description) Dimension
Motivation at the beginning = 500 Person/Year
Capacity at the beginning = 250 Person/Year
Enrollment base = 250 Person/Year
Employment at the start = 80 Person/Year
Motivated at the beginning = 420 Person
Studying at the beginning = 1250 Person
Graduated at the beginning = 80 Person
Employed at the beginning = 680 Person
Successful at the beginning = 270 Person
Quality at the beginning = Dmni
Elasticity = (0,0.7),(1,1),(3,1.5) Dmni
Factor 2 = 0.02 Dmnl
Factor 3 =0.31 Dmnl
Wait time =19 Year
Stay time 0 =0.5 Year
Stay time 1 =10 Year
Stay time 2 =20 Year
Effect time =3 Year
Expansion profile = (0,0),(2,0),(3,1),(10,1),(11,0),(30,0) Dmni
Table 7: Constants (Intervention)
Variable Equation (Description) Dimension
Expansion = Fraction of expansion Dmni
Employment expansion = (0,0),(30,0) Dmni
4.3 Discussion
The behavior of our model can be discussed based on different scenarios. The initial parameter
setting creates an equilibrium situation where the Motivated remains at 420, Studying
remains at 1250, Graduated remains at 80, Employed in related areas remains at
680 and Successful remains at 270. The Quality of engineering education stays at level 1
throughout the simulation run, as shown in Figure 7.
Becoming a successful engineer
1,000 -10% 10% +10%
-10%
-10%
Motivated
Studying
Employed
Successful
Quality
Figure 7: Model behavior
Two other simulation runs are carried out based on two other scenarios: one is that we expand our
enrollment by 10% and the other is that we contract our enrollment by 10% (Expansion by -
10%), as shown in Table 8.
Table 8: Scenario setting
Category Input values of Scenario Variables
Base: No change (BAU)
$1: Expansion Expansion = 10%
2: Reduction Expansion = -10%
In the scenario enrollment expand by 10%, we find that the Studying increases immediately.
However, the Quality of engineering education starts to fall. This in turn causes the
Motivated to drop and over a span of several years, the Studying actually decreases to a
level even lower than before the enrollment expansion.
On the other hand, in the scenario enrollment contraction by 10%, the opposite happens. The
Studying decreases soon after the implementation of the policy. But the quality of engineering
education increases as students now have more resources per person. This then raises the starting
salary for engineering graduates and then causes the Motivated to increase and over a span of
several years, the Studying actually comes to the same level as before the enrollment
contraction. The quality of engineering education increases by 20% over the simulation time
period. Even though the Employed in related areas drops a little bit at the beginning, it is higher
in later years.
5. Conclusions
5.1 Insights for policy makers
When facing an increasing need for engineers in industry, an intuitive policy would be to enlarge
the enrollment of engineering students and this is what the Chinese government did. However,
the system dynamics model and its simulation results clearly show counterintuitive results: China
is worse off with an enlarged enrollment. When the universities admit more engineering students,
the teaching facilities, especially those for practical learning, such as labs, are not enough. This
forces many courses to change from practical learning to theoretical learning. The quality of the
engineering education is significantly reduced. As students have less practical experience, they
are less capable in the eyes of the employers, thus reducing the starting salary of engineering
students. Many graduates work in unrelated areas making these four-year bachelor studies less
valuable to them than it should be. Fewer students are motivated to study engineer and fewer
students can be enrolled. In contrast, if the universities improve their practical teaching, making
students more qualified for their future work, then the graduates could have a higher starting
salary because of more demand for engineers on the job market. Better equipped from the
beginning, the students will have a better chance to be successful in their future career. In this
way, being an engineer would be an attractive career path for more people, which could
eventually reduce the gap of engineer demand and supply and also reduce the gap between
engineering education and real world practice.
5.2 Findings for system dynamics model building
During the model development process, we used a questionnaire to collect quantitative data and
interviews to collect qualitative data. Both quantitative data and qualitative interviews provided
us with a sound basis for our modeling work. Especially the detailed information obtained from
the interviews were helpful for the building of this system dynamics model.
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Conclusions on supporting change
If Image Theory offers a credible explanation of parental behaviour through different decision
making styles verified in AHP surveys, then their activity in eliminating risk for their child and the
family should mean a powerful and direct enabling mechanism is now at the disposal of others
traversing the admissions process, given sufficient reliable comparative data. Placing circumstantial
concerns aside, information gathered by families on each respective schools profit can similarly be
applied on the risk-reduced choice set to sequence the preferred order of the schools listed on the
application CAF. Rather than satisfying parent wellbeing deficit demands alone, children can get
involved at this stage of decision making as evidenced by one of the case studies.
Such sequencing of schools preferences by the student is a powerful enabler as it offers rapid
feedback to the child that their views are valued and trusted by parents who have already effectively
contributed their insights on the choice of secondary schools. This may serve to bolster levels of
child wellbeing and add to confidence that the most preferred option offers the best fit to need for
parent and child alike. A designed policy test should determine whether this is the case and could
therefore be recommended for example.
Modelling multi-methodology involving system dynamics
A viable multi-method approach has been explained that enables those involved in choosing their
secondary schools and agencies from education, health and local authorities involved supporting
their decision making process to understand what impact shared policies can have on reducing
unwanted shocks in the system. System dynamics enables lessons from successful parent decision
making examples to be incorporated alongside views from expert practitioners.
Applied multi-method validation technique
Case study validation and specifically AHP structured survey results relies upon accurately capturing
parent perceptions and assessing consistency of weightings applied. This validation takes place at
the individual level as different family needs for secondary education could show inconsistencies
between different decision strategies for results being combined together. Importantly the
technique specifically questions detailed numerical scores for alternative options including beyond
the choice set at last place and fourth position.
Simulation model validation requires multiple agents to accept that the model exhibits behaviour
that they would anticipate or if not, why the model might challenge their mental model assumptions
surrounding. After conducting a range of confidence tests upon the model, asking participants to
complete a workbook (one that summaries the research contributions before describing resulting
simulation outcome) provides the ultimate form of simulator validation. Appendix B gives a
validation workbook example for understanding the capability of the system modelled.
Implications for modelling outcomes
Feedback from anticipated dynamics system responses to policy change for each agency is
important. However families being able to assimilate lessons learnt from their own decision
weighting pre-experience is needed to allow them to challenge their own assumptions about which
schools would offer real education potential for their child’ development. Parent exit survey results
are required to confirm or adjust the dynamic model of parent child relationships mediated by
knowledge and the template is suggested in Annex C.
15
It is concluded that the aim of paper for validating approach to address gap in K12 enrolment
dynamics is met through this approach.
Limitations of study
Feedback from parents involved in competitive admissions is important in making risk free school
choices and profitable sequencing for most preferred school where the gaming approach is the
suggested way forward to gain this confidence. This study is limited to the confines of the secondary
education system in Plymouth for the range and variety of state school options available to families.
Results are incomplete at this stage and may not demonstrate learning to reapply to those
encountering the admissions system for the first time. Dynamic wellbeing relationships between
parent and child may be generalised but this remains to be confirmed.
Double Loop Learning for organisations applies as much to competing Plymouth parents as it does to
urban admissions into secondary schools. Here priority choices lead to resulting offers being
achieved. Results then help shape future priority choices of schools in the first loop aimed at
complying with competition rules. However when target setting; parents use decision maker beliefs
to guide priority choices. Without a mechanism to feedback from resulting offers achieved, decision
makers and families involved in deciding secondary schools have little opportunity to pre-experience
the issue and modify their long-held beliefs or mental models. A mechanism for parent and child to
pre-experience decision considerations is one way to close the target setting loop and provide
double loop learning.
ANNEX A. Second Workshop Script
ANNEX B. Example Workbook Validation Tool
ANNEX C. Parent Survey
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