NewMetro Project embeddiNg kEts and Work based learning into MEchaTROnic profile Project n. 600984-EPP-1-2018-1-

IT-EPPKA2-SSA

WP1 Report

This documents reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained there in. SUMMARY

Primary activities

Methodology

Primary findings

Survey

Survey results

Examples of data analysis.

Findings from the analysis of materials

Partner countries situation

Preliminary suggestions

APPENDIX 1 Survey results from Rezekne Academy of Technologies

Primary activities

The aim of WP1 was to identify the key factors that can influence the development of a new curriculum in mechatronics.

The project foreseen two primary activities: • reviewing relevant surveys, articles, and projects connected with mechatronics

competences; • identifying the current skills gaps in order to define the framework of competence

for the international curriculum. More in details, partners:

• Collected education/training programs related to the MECHATRONICS domain providing by a two-page description of the main curricula identified, with teaching hours/discipline and internship.

• Reviewed existing studies, statistics, articles covering the issue, and identifying the specific skills and competencies requirements for the current workforce and young people to be recruited.

• Carried out a SWOT analysis of the present education/training provision concerning the existing and emerging demand of skills and competencies.

• Performed structured interviews. Methodology:

In order to extract relevant research from the published literature, a systematic literature search was undertaken between January 1st, 2015, and April 30th 2019. From this, we collected a large number of significant articles published in Scopus and ISI Web of Science conference proceedings, as well as in the databases of leading world publishers such as WileyOnline, IEEE Xplore DL, ACM DL, SAGE, and the AIS e-Library. We also used Google Scholar in order to integrate the results obtained, and evaluate the popularity of articles, taking account of their citations.

We also collected studies and results of European projects available on the portal of the European Commission and on the web.

We searched for relevant materials by adopting a simple search criterion using the terms “mechatronics”, “mechatronics education”, “mechatronics curriculum”. We then analyzed the collected materials, eliminating items that were inconsistent or that referred to overly generic issues. Finally, we obtained a collection of materials that we analyzed, taking into account:

• Empirical or theoretical results • Reliable experimentation • Technological architecture • Educational methodologies • Applicability The structured interviews were based on questionnaires defined by partners. Each partner

involved about 50 respondents (students, workers, stakeholders). Primary findings

From our analysis, emerged that, over the last few years, interest in mechatronics has been reinvigorated due to the advances in sensing, communication, and computing.

The extension of internet connectivity to physical devices has brought to the spread of the Internet of Things (IoT), a notion that encompasses everything is connected to the internet. Devices equipped with smart sensors can communicate with each other as well as with people who use smartphones or wearables.

Collected data shows that, nowadays, the scope of mechatronics is very vast and relates to multiple fields and domains

• The Medical field: in areas such as surgery, radiology, and emergency medicine; • Robotics Industry: for industrial robots and robotics systems; • Automotive/Automobile engineering: in the design and manufacture of motorcycles,

automobiles, and trucks that integrate mechanical, electrical, electronic, and software technologies as well as safety engineering;

• Research Organizations: with uses in instrumentation and sensors, microfluidic systems and MEMS (Micro-Electro-Mechanical Systems), and energy conversion;

• Mechanical Industry: with uses in designing, analyzing, manufacturing, and maintaining mechanical systems;

• Computer-Aided Design (CAD): with the use of computer systems to support and optimize mechanical design.

• Manufacturing Industry: in the production of industrial goods, machines, or tools; • Mining: with uses in the extraction of valuable minerals or other geological materials

from the earth; • Inspection: for uses such as the inspection oil and gas pipelines via drones.

To design the competency model for mechatronics, partners carefully analyzed the European

multilingual classification, ESCO (European Skills, Competences, Qualifications, and Occupations).

For mechatronics, ESCO indicates robotics as a key knowledge. Robotics is defined as a part of mechanical engineering, electrical engineering, and computer science that overlaps with mechatronics and automation engineering.

The skills and competence of a mechatronics engineer given by ESCO are: • maintain mechatronic equipment; • test mechatronic units; • install mechatronic equipment; • develop mechatronic test procedures; • simulate mechatronic design concepts; • calibrate mechatronic instruments; • assemble mechatronic units; • micro-mechatronic engineering.

Manufacturing challenges concern the introduction of advanced manufacturing processes including photonics and robotics.

We analyzed some significant examples of bachelor’s degree in mechatronics available in Europe:

• Fontys University of Applied Sciences (Bachelor in Mechatronics), Netherlands, Eindhoven

• Warsaw University of Technology (B.Sc. Automotive Mechatronics), Poland , Warsaw • October 2019 • Warsaw University of Technology (B.Sc. Photonics Engineering), Poland , Warsaw • Diploma University of Applied Sciences (Bachelor of engineering mechatronics),

Germany, Bad Sooden-Allendorf • University of Southern Denmark (Bachelor in Engineering - Mechatronics (BSc)),

Denmark Sønderborg • University of Southern Denmark (Bachelor in Mechatronics (BEng)),

Denmark Sønderborg • Kaunas University of Technology (Bachelor of Science in Mechatronics), Lithuania

, Kaunas • Bahcesehir University (Bachelor in Mechatronics Engineering), Turkey , Istanbul • Atilim University (BSc in Department of Mechatronics), Turkey, Ankara • Okan University (Bachelor in Mechatronics), Turkey, Istanbul • Vilnius Gediminas Technical University (Bachelor of Mechatronics and Robotics),

Lithuania, Vilnius • Rezekne Academy of Technologies (Bachelor - Mechatronics), Latvia, Rēzekne

From our research emerged that in mechatronics industry, most trends focus on ‘less’: less power consumption, less weight, less volume and lower costs. Product competition is therefore developing particularly strongly in these areas. At the same time, the strong growth within this industry is leading to rapid growth in the number of suppliers and systems, thus creating a clear risk of substitution. Given the highly customized nature of mechatronic products, the strength of competition between mechatronic systems is difficult to measure.

Our research also confirmed what emerged from a recent survey (Smith, 2016) which revealed that the majority of Americans predict that robots and computers will do much of the work that is currently performed by humans within 50 years (Figure 1), even if most workers (80%) expect that their own job or profession will remain largely unchanged, and will still exist in their current form 50 years from now (Figure 1).

Figure 1. % of workers in each group who say that robots and computers will do much of the work currently done by humans in 50 years (source: Pew Research Center, survey conducted June 10 – July 12, 2015).

Survey A methodological note Remarkable and precious has been the contribution of the partner Hanse Parliament by Dr. Max Hogeforster. In the kick-off meeting, he proposed to use the Survey Monkey Premium program and began the responsible de facto for many results of the survey. Dr Max Hogeforster collect data and elaborated statistics that we used to carry out a comparative analysis.

Accordingly, we have been able to use the Cronbach’s alpha to assess the internal consistency of the questionnaires only for Latvia, Greece, Catalogna, and Confindustria since these partners sent raw data. We also use the Kolmogorov–Smirnov test results (p < .05) to compare these data with a defined reference probability distribution.

Survey results

All partners performed the task of the regarding the survey. RTA produced data analysis for the surveys of the following partners: - Latvia - Greece - Confindustria - Catalonia Hanse Parliament provided statistics for the other partners.

RTA produced a comparative analysis using all data and statistics provided by partners. The results of the activity of Hanse Parliament are available on the SurveyMonkey site.

Examples of data analysis. Greek partner data analysis shows that the most important fields of technology are believed to be Advanced design systems and manufacturing integration (modelling, simulation, virtual testing, data management) (Mean .41), Industrial design with advanced materials (e.g. biomaterials, metals, ceramics, polymers, powders and Collaborative robotics for industrial application (in both cases Mean .32).

technological areas

All (N=59) students/workers (N=42)

Employers (N=17)

Mean

number of cases

Mean

number of

cases Mean

number of

cases 1. Industrial design with advanced

materials (e.g. biomaterials, metals, ceramics, polymers, powders

.32 19 .29 12 .41 7

2. Microelectronics applied in the mechanical systems industrial sector .15 9 .10 4 .29 5

3. Assembly lines management (automation, supervision, measurement and data transmission and storage, etc.) in the mechanical systems industrial sector

.26 15 .27 11 .24 4

4. LCA (Life Cycle Approaches) prognostics and environmental footprint evaluation

.17 10 .17 7 .18 3

5. Advanced design systems and manufacturing integration (modelling, simulation, virtual testing, data management)

.41 24 .43 18 .35 6

6. Collaborative robotics for industrial application .32 19 .33 14 .29 5

7. Advanced Design Systems (based on modeling, simulation, virtual prototyping, rapid prototyping, interaction with mixed reality technologies, etc. )

.24 12 .24 9 .24 4

8. Integration of manufacturing process with real time available data (data management, machine learning, etc. )

.27 16 .33 14 .12 2

9. New equipment for telecare, telemedicine, and telerehabilitation based on intelligent mechanical objects

.27 16 .26 11 .29 5

10. Domestic robots based on the Internet of Things paradigm .17 10 17 7 .18 3

11. Applications in the scope of Smart and Connected Communities .22 13 .17 7 .35 6

Catalonia partner data analysis shows that the most important fields of technology are believed to be Collaborative robotics for industrial application (Mean .55), Assembly lines management (automation, supervision, measurement and data transmission and storage, etc.) in the mechanical systems industrial sector (Mean .39) and Industrial design with advanced materials (e.g. biomaterials, metals, ceramics, polymers, powders (Mean .32). Confindustria partner data analysis shows that the most important fields of technology are believed to be Assembly lines management (automation, supervision, measurement and data transmission and storage, etc.) in the mechanical systems industrial sector (Mean .41), Collaborative robotics for industrial application (Mean .36) and Integration of manufacturing process with real time available data (data management, machine learning, etc.) (Mean .35). Latvia partner data analysis shows that the most important fields of technology are believed to be Collaborative robotics for industrial application (Mean .63), Microelectronics applied in the mechanical systems industrial sector (Mean .47) un Assembly lines management (automation, supervision, measurement and data transmission and storage, etc.) in the mechanical systems industrial sector (Mean .42). It is interesting that Confindustria, Latvia, Catalonia, and Greece show similar priorities. We can assume that these priorities are quite independent from the dimension of the countries.

TOP5 of the most important competencies according to all the respondents is the following:

1) System design and integration/interfacing between electronic and mechanical components (assemble and test mechatronic units, set up machine controls, customise software, adjust engineering design) (Mean .67);

2) Robotics programming (set up automotive robot) (Mean .43); 3) Design virtual testing and validation using modelling and simulation tools (simulate

mechatronic design concepts, use CAM software) (Mean .41); 4) PLC programming (program a CNC controller) (Mean .40); 5) Aptly choosing advanced materials that can suit product or process needs (new)

(Mean .39). In the identification of the most important competencies, statistically significant differences (p=.049) were found according to respondent group in the evaluation of the competency PLC

programming (program a CNC controller): employers consider it more important (Mean Rank 116.14) than students/young employees do (Mean Rank 100.62). The results show that according to the employers, only Ability to work with specialised design and mechatronic machine control software is over the mean (Mean 3.03) Examples of statistics produced by Hanse Parliament (Dr. Max Hogeforster)

This statistic shows a difference with the data analysis results: Collaborative robotics for industrial application is not in the three most important fields of technology. Findings from the analysis of materials

Partners selected relevant documents, e.g,:

• Hanse Parliament the document Prüfungs- und Studienordnung (PSO) Mechatronik 2015 (MEC);

• Confindustria the document “ Report on assessment skills and needs in the mechatronics and metallurgical sectors industries in the 5 Countries”, edited by Diego Santaliana (Technology Park of Pordenone) and Riccardo Zanelli (COMET).

Very useful has been the article “A Survey of Mechatronics Education in the Nordic and Baltic Countries” by Ottestad, Hovland, Persson, Robbersmyr, and Pohl.

From the collected materials emerged that, traditional courses in engineering as well as the mechatronic courses currently on offer are compartmentalized and taught by an individual instructor. In the near future, this approach will be inadequate since the solution of real problems invariably requires integrating different subjects and disciplines, both technical and non-technical.

The current distinct engineering modules should be revised and improved in order to bring about a seamless transition from university to industry and society.

Partner country situations

All partners prepared a report to illustrate the situation in their countries.

In this report, they organized data following a shared framework.

The analysis of the country reports shows many points of similarity.

All partners are aware of the ongoing changes in the labor market and are persuaded of the necessity to invest in education to support employment. A few of them quoted the recent research conducted by Frey and Osborn in order to evaluate how susceptible jobs are to computerization. From this research emerged that the least susceptible to computerization are both generalist occupations requiring knowledge of human heuristics and specialist occupations involving the development of novel ideas and artifacts (Frey & Osborne, 2017).

Partners underlined that it has been estimated that 50.7% of jobs have an opportunity for automation, which translates into a significant risk of lack of updated skills.

Accordingly, all partners share the opinion that an effort is needed to understand the future technology trends and develop strategies to support the changes in the industry.

All the interviewed employers pointed out that mechatronics is a future-relevant subject and introducing Learning 4.0 as a very useful tools for learning in mechatronics courses.

In many countries, a reform of the system of the Professional Education is in progress (Italy, Latvia, Germany).

In all countries there are several possibilities to get education in the fields of Mechatronics in VET and HEI.

In Austria and Catalonia there are trainings for employed persons as well as job-accompanying and apprenticeship opportunities.

Due to the interdisciplinary character of mechatronics, in all partner countries there are many specialized curricula that share basic contents.

Most curricula are based on general curricula provided from the governmental authorities (Ministry of Education, Science and Research).

All partners’ educational systems provide studies in mechatronics from level 5 to level 7.

Italy presents a very diversified and rich educational system. For instance, Regional Vocational Education and Training (IFP) courses do not refer to national study programs. The main IFP offer, which is an alternative to the school offer, is organized in two large areas: courses organized and run by training agencies accredited by the Regions and courses organized and run by upper secondary vocational institutes in partnership with training agencies. In the second case, schools follow the guidelines of their Regions for the organization of these courses1. After the qualification, students decide if to continue with the study and obtain the diploma or begin to work.

In Austria, the Mechatronics Cluster has been created that includes more than 300 companies.

Based on the NewMetro questionnaire, Austrian respondents are interested in:

- participating in trainings at the workplace (80%) - paying for an employee’s further education (60%) - Paying for a study leave 1 x a year (40%) -

IT GER LV AUS GRE CAT Mechatronics in VET X X X X X X Mechatronics in HEI X X X X X X Job-accompaning X X Apprentice X X

1 The Italian Education System, MIUR – INDIRE - Italian Eurydice Unit http://www.indire.it/lucabas/lkmw_img/eurydice/quaderno_eurydice_30_per_web.pdf

opportunities Ongoing VET reform X X X Curricula approved by governmental authorities

X X X X X X

The data of country reports are available on Google drive.

Preliminary suggestions

• Invest in teaching staff professionalization, e.g., integrating their competence. • Define and experiment more appropriate teaching-learning strategy based on

transformative learning. Indeed, individual learning styles (e.g., visual, auditory, kinesthetic) impact learners’ preferences and results, whilst there is evidence that people’s experiences of digital education are patterned distinctly in relation to social class, race, and disability. As such, online learning environments do not unproblematically reduce differences between individuals.

• Invest in Computational Thinking (CT). According to Wing, thinking computationally is a fundamental skill for everyone, not just computer scientists. Indeed, CT is a method of analytical thinking that encompasses many skills, such as designing algorithms, decomposing problems, and modeling phenomena. It can take place without a computer since it is “a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science” (Wing, 2006, p. 35).

• Enhance competences in psychology and anthropology. It is important to understand human behavior to design effective new integrated products. For instance, when designing new services, developers need to pay attention to the characteristics of classes of users, e.g., elderly or disabled people. Lack of domain knowledge and less usable interfaces may discourage the use of ICT based services.

• Introduce ethics. The construction of algorithms that take decisions needs to introduce and discuss ethical implications. There is a responsibility in the implementation of automatic decision systems. For instance, robot ethics encompasses ethical questions about how humans should design, deploy, and treat robots. Indeed, machine morality encompasses questions about what moral capacities a robot should have and how these capacities could be computationally implemented.

• Define a smart learning environment for mechatronics education should be a technology-enhanced teaching-learning system that simulates the real-world, allows access to different types of resources, provides collaborative functions, and can be easily adapted for work-based learning.

APPENDIX 1 Survey results from Rezekne Academy of Technologies

Here following is reported the comparative data analysis of the survey conducted by four project partners, Latvia, Italy, Greece, and Catalonia. The other partners, Austria, Germany, Fimeccanica (Italy), and Poland submitted the data in a different format, which could not be used for this particular method. Their data is reported in the SurveyMonkey site.

Methodology The survey results obtained were encoded. The results were processed using SPSS 25.0

software. At first, Cronbach’s alpha test was performed to determine the internal consistency of the

survey. In this case α =.857, which is a good coefficient. Kolmogorov–Smirnov test results were p < .05, which is why nonparametric tests were

used in data analysis. The response distribution was analysed and statistical significance of differences

depending on respondent profile was determined using the Kruskal–Wallis and Mann–Whitney test.

Two groups of respondents were involved in the survey – students/young employees and employers.

Student/young employee group description 158 student/young employee questionnaires were received. 3 questionnaires were deemed

invalid because they were completed incorrectly. 155 questionnaires were processed: 57 respondents from Latvia, 31 from Italy, 42 from Greece, and 25 from Catalonia. 90 respondents in this group were aged between 18 and 25, 51 respondents were between 26 and 35, 8 respondents were over 35, and 6 respondents did not wish to state their age.

The respondents had different education levels: 63 respondents had a secondary school education, 19 had post-secondary non-higher education (e.g. ITS or other EQF 5 Qualification), 56 had a Bachelor’s degree, 9 had a Master’s degree, 8 respondents stated that they had a different kind of education.

22 respondents had no work experience, 49 responds had less than one year of work experience, 56 had 1 to 5 years, but 28 respondents had more than 5 years of experience. We have to note that the work experience of 59 respondents was unrelated to the field of their education.

Employer group description 58 employer questionnaires were received: 10 respondents from Latvia, 17 from Italy, 17

from Greece, and 14 from Catalonia. 10 respondents were company owners, 14 were company managers, 9 were leading specialists (HR), 16 were leading specialists (innovation manager), and 9 were leading specialists (other).

The respondents had different numbers of subordinate employees: 25 respondents had up to 50 employees, 6 respondents had 52 to 100 employees, 6 had 101 to 200 employees, 6 had 201 to 500 employees, 7 had 501 to 1000 employees, 6 had 1001 to 2000 employees, 1 respondent had 2001 to 5000 employees, and 1 had more than 5000 employees.

The age of the respondents in this group varied: 15 respondents were aged between 26 and 35, 16 respondents were aged between 36 and 45, 17 respondents were aged between 46 and 55, 10 respondents were over 55.

51 respondents were male, 6 were female, one respondent did not wish to state their age. The respondents had different education levels: 12 respondents had a Bachelor’s degree, 33

had a Master’s degree, 3 had other education, the majority stated that they had a PhD. The differences in the profiles of the respondents in both groups allow obtaining a more

comprehensive opinion on the research phenomenon.

ANALYSIS RESULTS

Respondents were offered to choose three fields of technology, which will be important in the future. Table 1 shows that the most important fields of technology are believed to be Collaborative robotics for industrial application (Mean .40), Assembly lines management (automation, supervision, measurement and data transmission and storage, etc.) in the mechanical systems industrial sector (Mean .37) and Advanced design systems and manufacturing integration (modelling, simulation, virtual testing, data management) (Mean .33).

Table 1 Important technological areas

technological areas

All (N=212) students/young employees

(N=155)

Employers (N=58)

differences (p)

Mean

number of cases

Mean

number of cases

Mean

number of cases

by grou

p

by count

ry 1. Industrial design with

advanced materials (e.g. biomaterials, metals, ceramics, polymers, powders

.31 65 .28 43 .38 22 .181 .101

2. Microelectronics applied in the mechanical systems industrial sector

.30 63 .31 48 .28 16 .589 .000

3. Assembly lines management (automation, supervision, measurement and data transmission and storage,

.37 79 .42 64 .29 17 .119 .159

etc.) in the mechanical systems industrial sector

4. LCA (Life Cycle Approaches) prognostics and environmental footprint evaluation

.11 23 .09 14 .16 9 .213 .117

5. Advanced design systems and manufacturing integration (modelling, simulation, virtual testing, data management)

.33 70 .32 50 .33 19 .914 .286

6. Collaborative robotics for industrial application .49 103 .48 76 .48 28 .806 .000

7. Advanced Design Systems (based on modeling, simulation, virtual prototyping, rapid prototyping, interaction with mixed reality technologies, etc. )

.21 42 .17 25 .28 16 .063 .038

8. Integration of manufacturing process with real time available data (data management, machine learning, etc. )

.28 60 .25 39 .34 20 .125 .703

9. New equipment for telecare, telemedicine, and telerehabilitation based on intelligent mechanical objects

.18 39 .19 29 .19 11 .970 .241

10. Domestic robots based on the Internet of Things paradigm

.14 29 .17 26 .07 4 .068 .006

11. Applications in the scope of Smart and Connected Communities

.13 28 .12 19 .16 9 .597 .005

Using the Kruskal–Wallis and Mann–Whitney test, it was determined whether there are

statistically significant differences in the evaluations according to respondents profile. No statistically significant differences were found according to respondent group (p>.05);

whereas several statistically significant differences were found according to the respondent’s country of residence:

• Statistically most significant differences (p=.000) in the evaluation of the field Microelectronics applied in the mechanical systems industrial sector: respondents from Latvia gave it the highest evaluation (Mean Rank 112.16); whereas respondents from Greece gave the lowest evaluation (Mean Rank 93.32). For comparison, the Mean Rank for the responses by Italian respondents was 11.39 and Catalonian – 108.14;

• Statistically most significant differences (p=.000) in the evaluation of the field Collaborative robotics for industrial application: respondents from Italy gave it the highest evaluation (Mean Rank 118.67); whereas respondents from Catalonia gave the lowest evaluation (Mean Rank 93.33). The Mean Rank for Latvia was 102.32, and Greece – 109.98;

• Statistically very significant differences (p=.006) in the evaluation of the field Domestic robots based on the Internet of Things paradigm: respondents from Italy gave it the highest evaluation (Mean Rank 119.06), followed by Greece (Mean Rank109.95) and Catalonia (Mean Rank 105.95); whereas respondents from Latvia gave the lowest evaluation (Mean Rank 95.16);

• Statistically very significant differences (p=.005) in the evaluation of the field Applications in the scope of Smart and Connected Communities: respondents from Greece gave it the highest evaluation (Mean Rank 115.47), respondents from Latvia gave the lowest evaluation (Mean Rank 94.08); for comparison, the Mean Rank for Italy was 108.29, Catalonia – 112.03;

• Statistically significant differences (p=.038) in the evaluation of the field Advanced Design Systems (based on modelling, simulation, virtual prototyping, rapid prototyping, interaction with mixed reality technologies, etc.): respondents from Italy gave it the highest evaluation (Mean Rank 112.59), respondents from Latvia gave the lowest evaluation (Mean Rank 101.81); whereas the Mean Rank for Greece was 106.53, Catalonia –107.18.

Differences within each country can be seen in case analysis. The respondents were offered to identify the most important competencies, which a

“mechatronics” technician should have. The mean values, number of instances, and differences according to respondent profile are summarized in Table 2.

Table 2 The most important competences that a “mechatronic” technician should have

technological areas

All (N=212)

students/young

employees (N=155)

Employers (N=58)

differences (p)

Mean

number of cases

Mean

number of cases

Mean

number of cases

by group

by count

ry

Aptly choosing advanced materials that can suit product or process needs (new)

.39 83 .40 61 .40 23 .990 .000

System design and integration/interfacing between electronic and mechanical components (assemble and test mechatronic units, set up machine controls, customise software, adjust engineering design)

.67 142 .71 99 .72 42 .270 .534

Evaluate environmental impact both from product manufacturing and product whole life cycle (new)

.25 53 .25 38 .26 15 .969 .746

Define pre-emptive maintenance protocols and early diagnostic maintenance protocols (maintain mechatronics equipment)

.27 58 .24 37 .34 20 .107 .294

Robotics programming (set up automotive robot) .43 90 .43 66 .19 24 .999 .079

Design virtual testing and validation using modelling and simulation tools (simulate mechatronic design concepts, use CAM software)

.41 86 .37 57 .07 28 .110 .254

Virtual Prototyping tools and techniques (Dynamic simulations, Human in the loop simulation, HMI, AR/VR, Digital Twin development)

.26 54 .23 35 .31 18 .147 .355

Base knowledge of Prototyping electronics boards and embedded systems

.27 58 .28 43 .24 14 .657 .001

Utilize, choose, customize monitoring and data management systems (monitor .automated machines, Record test data)

.21 45 .21 33 ,21 12 .810 .347

PLC programming (program a CNC controller) .40 85 .37 56 .50 29 .049 .240

Low-Level programming base skills (C, C.19++, etc..) .22 46 .23 35 .17 10 .462 .001

High-level programing base skills (Pyton, Java, etc..) .18 37 .18 27 .17 10 .874 .001

Carry on diagnostic activities interfacing machines/assembly .29 61 .28 43 .33 19 .287 .608

lines and collecting data by LabView-like tools (resolve equipment malfunctions, maintain control systems for automated equipment, perform test run) Access a data base using SQL (record test data) .10 20 .10 15 .09 5 .736 .129

Evaluate ergonomic aspects of industrial logistics (workloads, movements, time constraints, use of mobile devices, etc.) (new)

.19 41 .21 33 .12 7 .174 .178

Evaluate advanced logistics project aspects like cost/benefit ratio, safety, impact, threats, maintenance, etc.) (follow safety standards, new)

.24 51 .25 38 .21 12 .635 .001

TOP5 of the most important competencies according to all the respondents is the

following: 6) System design and integration/interfacing between electronic and mechanical

components (assemble and test mechatronic units, set up machine controls, customise software, adjust engineering design) (Mean .67);

7) Robotics programming (set up automotive robot) (Mean .43); 8) Design virtual testing and validation using modelling and simulation tools (simulate

mechatronic design concepts, use CAM software) (Mean .41); 9) PLC programming (program a CNC controller) (Mean .40); 10) Aptly choosing advanced materials that can suit product or process needs (new)

(Mean .39). In the identification of the most important competencies, statistically significant

differences (p=.049) were found according to respondent group in the evaluation of the competency PLC programming (program a CNC controller): employers consider it more important (Mean Rank 116.14) than students/young employees do (Mean Rank 100.62).

As a result of the Kruskal–Wallis test, several statistically significant differences were found according to the respondent’s country of residence:

• Statistically most significant differences (p=.000) in the evaluation of the competency Aptly choosing advanced materials that can suit product or process needs (new): respondents from Catalonia rated it the highest (Mean Rank 139.46), followed by respondents from Latvia (Mean Rank 111.74) and respondents from Greece (Mean Rank 96.15); whereas respondents from Italy gave it the lowest evaluation (Mean Rank 82.85);

• Statistically most significant differences (p=.001) in the evaluation of the competency Base knowledge of Prototyping electronics boards and embedded systems: in this case,

respondents from Latvia gave the highest evaluation (Mean Rank 121.09); whereas respondents from Catalonia gave the lowest (Mean Rank 88.11). For comparison, the Mean Rank of respondents from Greece was 110.41, but Italy – 93.05;

• Statistically most significant differences (p=.001) in the evaluation of the competency Low-Level programming base skills (C, C.19++, etc.): respondents from Catalonia gave the lowest evaluation (Mean Rank 90.79); respondents from Latvia rated it a little higher (Mean Rank 95.04); whereas the evaluation by respondents from Italy and Greece was equally high – Mean Rank 117.50 each;

• Statistically most significant differences (p=.001) in the evaluation of the competency High-level programing base skills (Pyton, Java, etc..): respondents from Italy gave the highest evaluation (Mean Rank 117.32); whereas respondents from Catalonia gave the lowest (Mean Rank 87.50). For comparison, the Mean Rank of respondents from Greece was 115.63, for respondents from Latvia – 100.10.

• Statistically most significant differences (p=.001) in the evaluation of the competency Evaluate advanced logistics project aspects like cost/benefit ratio, safety, impact, threats, maintenance, etc.) (follow safety standards, new): respondents from Italy gave the highest evaluation (Mean Rank 128.66), whereas respondents from Catalonia gave the lowest (Mean Rank 94.38). For comparison, the Mean Rank of respondents from Greece was 101.60, respondents from Latvia – 100.97.

Differences within each country can be seen in case analysis. The respondents were offered statements about mechatronics, which had to be evaluated on

the Likert scale from 0 to 4, where 0 means “I completely disagree”; 1 – “I somewhat disagree”; 2 – “I neither agree nor disagree”; 3 – “I somewhat agree”; 4 – “I completely agree” (see Table 3).

Table 3 Respondents' assessment of statement about mechatronics

Statement

Mean differences (p)

All (N=212)

students/young employees (N=155)

Employers (N=58)

by group

by country

Mechatronics can be considered an independent discipline 2.24 2.09 2.66 .014 .000

A challenge in mechatronics as a study discipline is to find the right organization and focus between courses in mechanical, electrical and computer engineering

2.79 2.64 3.12 .035 .000

Mechatronics study programs are appropriate and effective 2.37 2.30 2.52 .711 .000

Mechatronics is a future-relevant subject 3.16 3.02 3.48 .329 .000

Interdisciplinary and systematic thinking play an important role in mechatronics

2.83 2.61 3.36 .000 .000

To create a mechatronics study program, teachers must be retrained 2.79 2.71 2.98 .072 .000

Cost to create a mechatronics study programs is high 2.64 2.72 2.48 .095 .000

A mechatronics study program is an opportunity to attract new students 3.12 3.05 3.29 .014 .306

Learning 4.0 is very useful in mechatronics curses 2.99 2.96 3.09 .357 .015

A smart learning environment is essential for mechatronics courses 3.06 3.10 2.95 .340 .292

As a result of the Mann - Whitney test, several statistically significant differences were

found according to the respondent’s group : • Mechatronics can be considered an independent discipline (p=.014): employers give

the highest evaluation (Mean Rank 122.14), students/young employees give the lowest (Mean Rank 99.73);

• A challenge in mechatronics as a study discipline is to find the right organization and focus between courses in mechanical, electrical and computer engineering (p=.035): employers give the highest evaluation (Mean Rank 119.39) , students/young employees give the lowest (Mean Rank 100.80);

• Interdisciplinary and systematic thinking play an important role in mechatronics (p=.000): employers give the highest evaluation (Mean Rank 131.56), students/young employees give the lowest (Mean Rank 96.08);

• A mechatronics study program is an opportunity to attract new students (p=.014): employers give the highest evaluation (Mean Rank 120.97), students/young employees give the lowest (Mean Rank 99.45).

Except for the evaluation of the statements A mechatronics study program is an opportunity to attract new students and A smart learning environment is essential for mechatronicscourses, the rest demonstrated statistically significant differences according to the respondent’s country of residence. The Mean Rank for each country is shown in Table 4 (red means the highest Mean Rank; whereas blue means the lowest).

Table 4 Description of statistically significant differences for the statements on

mechatronics by country

Statement p Mean Rank

Latvia Italy Greece Catalunia Mechatronics can be considered an independent discipline .000 129.00 110.51 103.23 53.76

A challenge in mechatronics as a study .000 129.00 106.66 115.15 52.97

discipline is to find the right organization and focus between courses in mechanical, electrical and computer engineering Mechatronics study programs are appropriate and effective .000 166.10 108.55 69.61 57.13

Mechatronics is a future-relevant subject .000 137.72 110.01 104.37 50.13 Interdisciplinary and systematic thinking play an important role in mechatronics .000 125.19 123.47 101.91 59.82

To create a mechatronics study program, teachers must be retrained .000 74.33 120.66 117.42 124.84

Cost to create a mechatronics study programs is high .000 120.30 86.56 84.32 141.86

Learning 4.0 is very useful in mechatronics curses .015 110.62 114.24 86.71 120.91

Students/young employees performed a self-assessment of the abilities required for the

competence of a mechatronics engineer (Actual Competence) and evaluated to what extent a particular ability needs to be improved to preserve competitive capacity (Importance), which had to be evaluated on the Likert scale from 0 to 4, where 0 means “I completely disagree”; 1 – “I somewhat disagree”; 2 – “I neither agree nor disagree”; 3 – “I somewhat agree”; 4 – “I completely agree”

Table 4 Mean of Assessment of ability/competence (students/young employees)

Ability/Competence Mean Actual Importance

Ability to design an automation process algorithm and prepare a technological task for machine design 2.22 3.03

Ability to work with specialised design and mechatronic machine control software 2.16 2.97

Ability to perform a visual assessment of the operation of mechatronic machines 2.32 2.93

Ability to design a mechatronic machine monitoring and visualisation system 2.01 2.84

Competence in the maintenance, diagnostics and repairs of automated machines 2.19 2.90

Ability to keep track of the number of spare parts of mechatronic machines and order these 2.16 2.81

Ability to design a production technology plan 2.14 2.91 Ability to assess the level of production automation 2.21 2.96 Ability to choose suitable materials, creating machine constructions 2.20 3.04 Ability to create software for the programming of automated system control elements 2.04 3.05

Ability to determine the operation precision of a mechatronic system 2.12 2.99 Ability to determine the lifetime of a mechatronic system 2.00 2.79 Ability to choose in the designing process the coupling sizes and allowances to ensure quality long-term operation of machines 2.04 2.98

Ability to assess the most economically advantageous technical solutions 2.23 3.03

Ability to estimate the costs of the machine in the design or creation process and set the term when the machine will pay for itself 2.07 2.85

Ability to plan work and organise its timely completion 2.44 3.19 Ability to organise and manage the work of staff 2.43 2.96 Ability to ensure the fulfilment of the environmental protection and health and safety law and regulation requirements 2.36 3.04

Ability to communicate in the official language and at least two foreign languages 2.65 3.05

Ability to study and understand the laws and regulations on the matters of machine safety 2.42 3.02

Competence in the International Organization for Standardisation (ISO) quality safety and environmental protection systems 2.38 3.06

The respondents believe most frequently that they possess such competencies as Ability to

communicate in the official language and at least two foreign languages (Mean 2.65), Ability to plan work and organise its timely completion (Mean 2.44), Ability to organise and manage the work of staff (Mean 2.43) and Ability to study and understand the laws and regulations on the matters of machine safety (Mean 2.42). Mean values show that the competencies Competence in the International Organization for Standardisation (ISO) quality safety and environmental protection systems, Ability to create software for the programming of automated system control elements, Ability to communicate in the official language and at least two foreign languages, Ability to choose suitable materials, creating machine constructions, Ability to ensure the fulfilment of the environmental protection and health and safety law and regulation requirements, Ability to assess the most economically advantageous technical solutions, Ability to design an automation process algorithm and prepare a technological task for machine design and Ability to study and understand the laws and regulations on the matters of machine safety are the ones the respondents would like to improve the most.

As a result of the Kruskal–Wallis test, many statistically significant differences were found in the evaluation of both Actual Competence and its Importance according to the respondent’s country of residence. These are summarised in Table 5 (red means the highest Mean Rank; whereas blue means the lowest; the cases where no statistically significant differences were found have not been analysed).

Table 5 Description of statistically significant differences according to the respondent’s country of residence in the evaluation of the Actual Ability/Competence and its Importance

(Student/young employee responses)

Ability/Competence

Actual Importance

p Mean Rank

p Mean Rank

Latvia

Italy Gree

ce Catalun

ia Latv

ia Italy

Greece

Catalunia

Ability to design an automation process algorithm and prepare a technological task for machine design

.000

66.36

63.70

79.10

115.23 .007

89.98

57.77 75.5

7 76.71

Ability to work with specialised design and mechatronic machine control software

.000

83.15

70.37

48.62

121.70 .000

98.71

66.82 61.3

1 69.25

Ability to perform a visual assessment of the operation of mechatronic machines

.000

86.16

66.65

51.63

112.58 .000

96.85

65.11 56.8

7 80.59

Ability to design a mechatronic machine monitoring and visualisation system

.000

74.59

77.00

60.77

109.76 .003

90.96

77.18 59.1

1 74.92

Competence in the maintenance, diagnostics and repairs of automated machines

.000

76.00

60.97

66.39

119.42 .001

94.58

70.00 61.7

7 74.15

Ability to design a production technology plan

.000

69.64

75.73

68.33

111.23 .024

87.80

74.52 62.3

3 83.44

Ability to assess the level of production automation

.001

74.81

67.60

68.69

108.50 .012

86.35

61.74 69.3

1 91.17

Ability to choose suitable materials, creating machine constructions

.000

83.80

68.67

55.63

108.67 .000

95.36

75.53 53.5

7 79.50

Ability to create software for the programming of automated system control elements

.000

67.57

69.70

71.21

118.65 .014

87.22

70.21 63.3

6 88.58

Ability to determine the operation precision of a mechatronic system

.002

81.30

71.08

61.38

101.52 .001

94.90

70.02 63.6

0 70.17

Ability to determine the lifetime of a mechatronic system

.001

69.80

82.02

66.20

106.73 .001

93.57

72.55 59.9

3 76.48

Ability to choose in the designing process the coupling sizes and allowances to ensure quality long-term operation of

.001

81.23

71.58

60.23

103.08 .019

91.53

68.03 69.4

8 70.46

machines

Ability to assess the most economically advantageous technical solutions

- - - - - .000

97.20

65.60 63.8

7 69.94

Ability to estimate the costs of the machine in the design or creation process and set the term when the machine will pay for itself

.000

73.29

71.50

66.30

111.42 .037

87.83

75.98 62.8

6 80.54

Ability to plan work and organise its timely completion

.004

87.49

62.58

64.99

91.13 .000

99.08

63.42 61.7

9 71.94

Ability to communicate in the official language and at least two foreign languages

.000

95.00

57.05

59.74

89.40 .000

95.73

71.34 59.3

9 73.85

Ability to study and understand the laws and regulations on the matters of machine safety

- - - - - .046

89.55

73.97 70.6

9 65.35

Competence in the International Organization for Standardisation (ISO) quality safety and environmental protection systems

- - - - - .015

91.14

71.76 64.8

9 74.58

The employers were also offered to evaluate the abilities of their employees –

mechatronics engineers – corresponding to their competence, which had to be evaluated on the Likert scale from 0 to 4, where 0 means “I completely disagree”; 1 – “I somewhat disagree”; 2 – “I neither agree nor disagree”; 3 – “I somewhat agree”; 4 – “I completely agree” (see table 5)

Table 5 Mean of Assessment of ability (employers)

Ability Mean

1. Ability to design an automation process algorithm and prepare a technological task for machine design 2.84

2. Ability to work with specialised design and mechatronic machine control software 3.03 3. Ability to perform a visual assessment of the operation of mechatronic machines 2.79 4. Ability to design a mechatronic machine monitoring and visualisation system 2.86 5. Competence in the maintenance, diagnostics and repairs of automated machines 2.78 6. Ability to keep track of the number of spare parts of mechatronic machines and order these 2.52 7. Ability to design a production technology plan 2.50 8. Ability to assess the level of production automation 2.72 9. Ability to choose suitable materials, creating machine constructions 2.84 10. Ability to create software for the programming of automated system control elements 2.81 11. Ability to determine the operation precision of a mechatronic system 2.81 12. Ability to determine the lifetime of a mechatronic system 2.52

13. Ability to choose in the designing process the coupling sizes and allowances to ensure quality long-term operation of machines 2.71

14. Ability to assess the most economically advantageous technical solutions 2.64 15. Ability to estimate the costs of the machine in the design or creation process and set the term

when the machine will pay for itself 2.66

16. Ability to plan work and organise its timely completion 2.83 17. Ability to organise and manage the work of staff 2.52 18. Ability to ensure the fulfilment of the environmental protection and health and safety law and

regulation requirements 2.84

19. Ability to communicate in the official language and at least two foreign languages 2.60 20. Ability to study and understand the laws and regulations on the matters of machine safety 2.64 21. Competence in the International Organization for Standardisation (ISO) quality safety and

environmental protection systems 2.62

The results show that according to the employers, only Ability to work with specialised

design and mechatronic machine control software is at the average level (Mean 3.03) above average. In the other cases the evaluation is below average.

No statistically significant differences were found in the evaluations of the respondents. Differences within each country can be seen in case analysis. In response to the question about the measures employers are ready to take in order to

promote the improvement of the professional competence of their employees, 13 respondents support training at the workplace; in 9 instances they allow the possibility of paying for an employee’s study leave once a year; in 5 instances – to pay for an employee’s further education.

The employers were asked to provide their comments/recommendations for the implementers of the education process. These are the following:

• Students need to be given more knowledge (on thermal processing, various processes in metal processing, especially new and innovative; on the construction of gears, on the creation of automation and searching for solutions and their introduction in manufacturing);

• Create a qualification gradation system, ensuring the cooperation between teaching staff and companies in the creation of the system;

• Perform the evaluation of student course work in cooperation between a Docent and a Company;

• Actively participate in the New Metro project with the aim to foster a liquid learning environment

The recommendations show that the employers are open to cooperation and interested in the quality preparation of specialists