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Enhancing engineering ethics education (EEE) for green intelligent manufacturing: Implementation performance evaluation of core mechanism of green intelligence EEE

Frontiers in Psychology

July 2022
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DOI:10.3389/fpsyg.2022.926133

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Authors:

Shi Yin

Hebei Agricultural University

The characteristics of green intelligent (GI) engineering ethics emphasize the necessity of GI engineering ethics education (EEE). The ethics education of GI engineering is in the development stage, and it is urgent to fully understand the significance of evaluating the development of GI EEE. Only based on the GI manufacturing situation system to understand the implementation status of the core education of EEE can we objectively grasp the improvement space of GI EEE. In this study, the corresponding indicators were selected from three dimensions of cultivation education, collaborative education, and situational education to form the element community of evaluation indicators. The fuzzy analytic hierarchy process and the fuzzy comprehensive evaluation method were used to empirically evaluate the implementation of the key mechanism of GI EEE. The results are as follows. (1) The key education of GI EEE includes cultivation education of micro dimension, collaborative education of medium dimension, and situational education of macro dimension. (2) The most important education is to strengthen the ethics education of GI engineering in the training process of college students. The coordination of GI EEE is becoming more and more important, and the integration and construction are the important pursuit of GI EEE. (3) The cultivation education, collaborative education, and situational education of GI EEE are all at a general level. (4) There is not only a gap between theory and practice in GI EEE but also insufficient attention to localization and coordination issues. The willingness of the government to participate in the ethical education of GI engineering is very insufficient. The optimized space of training education includes teaching cases and full-cycle ethical education.

A core mechanism of GI EEE.

…

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TYPE Original Research

PUBLISHED 27 July 2022

DOI 10.3389/fpsyg.2022.926133

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Rensselaer Polytechnic Institute,

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Nan Zhang

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CITATION

Yin S and Zhang N (2022) Enhancing

engineering ethics education (EEE) for

green intelligent manufacturing:

Implementation performance

evaluation of core mechanism of

green intelligence EEE.

Front. Psychol. 13:926133.

doi: 10.3389/fpsyg.2022.926133

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the terms of the Creative Commons

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does not comply with these terms.

Enhancing engineering ethics

education (EEE) for green

intelligent manufacturing:

Implementation performance

evaluation of core mechanism

of green intelligence EEE

Shi Yin1*and Nan Zhang2*

1College of Economics and Management, Hebei Agricultural University, Baoding, China, 2School of

Marxism, Hebei Agricultural University, Baoding, China

The characteristics of green intelligent (GI) engineering ethics emphasize the

necessity of GI engineering ethics education (EEE). The ethics education of GI

engineering is in the development stage, and it is urgent to fully understand

the signiﬁcance of evaluating the development of GI EEE. Only based on the

GI manufacturing situation system to understand the implementation status

of the core education of EEE can we objectively grasp the improvement

space of GI EEE. In this study, the corresponding indicators were selected

from three dimensions of cultivation education, collaborative education, and

situational education to form the element community of evaluation indicators.

The fuzzy analytic hierarchy process and the fuzzy comprehensive evaluation

method were used to empirically evaluate the implementation of the key

mechanism of GI EEE. The results are as follows. (1) The key education of GI EEE

includes cultivation education of micro dimension, collaborative education

of medium dimension, and situational education of macro dimension. (2)

The most important education is to strengthen the ethics education of GI

engineering in the training process of college students. The coordination

of GI EEE is becoming more and more important, and the integration and

construction are the important pursuit of GI EEE. (3) The cultivation education,

collaborative education, and situational education of GI EEE are all at a general

level. (4) There is not only a gap between theory and practice in GI EEE but also

insucient attention to localization and coordination issues. The willingness of

the government to participate in the ethical education of GI engineering is very

insucient. The optimized space of training education includes teaching cases

and full-cycle ethical education.

KEYWORDS

green intelligence, engineering education, ethical education, fuzzy analytic hierarchy

process, performance evaluation

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Yin and Zhang 10.3389/fpsyg.2022.926133

Introduction

In recent years, various engineering accidents occur

frequently, and many engineering technologies, such as

transgenic technology, embryo technology, and artiﬁcial

intelligence, have generated more and more ethical disputes

(Yin et al., 2020). There are also multiple ethical risks in

engineering practice, such as the environmental risks brought

by the integration of various technologies and their application

to nature, and the quality and safety risks of using technologies

to build artifacts (Martin et al., 2021). Due to the technical

complexity and social connection of engineering itself,

engineering ethics is no longer a problem within engineering

but related to the survival and development of the whole

human society. Mote, a member of the US National Academy

of Engineering, pointed out that engineering in the twenty-ﬁrst

century is a combination of the technology part “creation and

solution” and the user part “people and society.” It is this ethical

load, including conscience and benevolence, that constitutes

the ultimate goal and value of engineering and promotes

its continuous development (Yin et al., 2020). The inherent

nature of the uncertainty in the development of science and

technology innovation will lead to more challenging ethical

problems in the future. The emergence of engineering ethics

problems is often caused by the lack of ethical awareness, the

insuﬃcient estimation of the consequences of engineering

activities, the conﬂict of interests of all parties in the project,

the weak consciousness of natural social responsibility, and

other factors, which all come from the subject of engineering

practice (Yin and Zhang, 2021). Engineers who are direct

participants in engineering practice often face ethical dilemmas,

and the work they do, such as designing, planning, and

managing infrastructure, as well as designing materials and

systems, involves risk (Trentesaux and Karnouskos, 2022).

Engineers, therefore, have a high degree of responsibility to

society and stakeholders. It is necessary to strengthen students’

understanding of engineering ethics in the teaching process

so that they can have a certain sense of ethics after engaging

in information related work, and can use the learned ethical

knowledge to analyze, judge, and make decisions on their

engineering practice (Hsu, 2020).

With the acceleration of the new technological revolution

characterized by “green and intelligent,” the integration

of green manufacturing and intelligent manufacturing

has become the key to the high-quality development of

manufacturing engineering. Intelligent manufacturing and

green manufacturing have become two major development

directions of contemporary manufacturing engineering

(Popescu et al., 2020). Although both serve the manufacturing

process, intelligent manufacturing and green manufacturing

have diﬀerent manufacturing concepts and priorities. The

intelligent manufacturing mode focuses on how to use

information ﬂow and data ﬂow in the manufacturing process to

endue manufacturing system with intelligence, thus improving

production eﬃciency and reducing operating costs. Green

manufacturing focuses on how to plan the material ﬂow

and energy ﬂow in the manufacturing process to improve

the resource utilization rate and green production eﬃciency

of the manufacturing system. This then coordinates the

economic beneﬁt and the social beneﬁt of the enterprise.

In actual production, intelligent manufacturing and green

manufacturing have synergism and complementarity. Intelligent

manufacturing and green manufacturing are two subsystems

belonging to manufacturing system from the point of view of

system goal synergy (Yin et al., 2022a). From the perspective

of system function complementarity, the intelligence brought

by intelligent manufacturing subsystem is conducive to

the rational planning and utilization of resources of the green

manufacturing subsystem. The low carbonization insisted by the

green manufacturing subsystem is a necessary condition for the

intelligent manufacturing subsystem to reduce cost and improve

eﬃciency. The key to the integration of green manufacturing

and intelligent manufacturing lies in the continuous innovation

and promotion of important green intelligent (GI) key generic

technologies in the ﬁeld of industrial engineering (He and Bai,

2021). Intelligent manufacturing endows manufacturing system

with new functions of self-organization, self-regulation, and

self-operation production intelligence. Green manufacturing

carries out dynamic planning of material ﬂow and energy

ﬂow for product research and development, production,

maintenance, recycling, and other manufacturing processes to

maximize green production eﬃciency in the manufacturing

process. In the process of GI manufacturing, the parallel

engineering and integration engineering of the intelligent and

green manufacturing process should be established to achieve

the dual goals of improving production eﬃciency and realizing

cleaner production (He and Bai, 2021). With the remolding of

traditional manufacturing engineering by GI manufacturing,

new requirements of engineering ethics also appear.

GI engineering ethics is the standard and guidance for

engineers and engineering activities, and its connotation

includes two aspects. On the one hand, the pursuit of goal

value is the commitment of engineering and engineers to

human progress and the pursuit of improving human wellbeing,

which is also the basis of engineering ethics (Trentesaux and

Caillaud, 2020). The other side contains various engineering

ethics rules and norms. These speciﬁc systems of ethical

principles inﬂuence the way project stakeholders live, how

decisions are made, and how those decisions later aﬀect human

society. Although the innovation and rapid development of GI

manufacturing technology and its application in engineering

will bring great contributions to the welfare of human society

subjectively, it will inevitably pose severe challenges to human

ethics (Iphofen and Kritikos, 2021). This forms an increasingly

sharp ethical problem of GI engineering. The problems mainly

include those caused by the use of information technology

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and artiﬁcial intelligence. The wide application of information

technology in the ﬁeld of engineering will impact the boundary

of traditional moral responsibility, thus causing ethical problems

that must be paid great attention to. The problems include

the possible infringement of copyright owners of various

works caused by digitization and Internet, the possible privacy

protection problems caused by the unrestrained dissemination

and collection of massive information to the public, and the

abandonment of traditional social ethical life by addicts in the

virtual network world (Royakkers et al., 2018). Improper use

of artiﬁcial intelligence technology may pose a great threat to

human society and trigger new ethical issues. For example,

robots challenge, threaten, and harm humans. Humanoid

robots impact human life and its way, and challenge law and

public order. Brain-computer interface technology and face

recognition technology bring personal privacy issues.

Compared with traditional engineering, GI engineering

has the typical characteristics of complexity, integration,

socialization, and globalization. GI engineering projects must

not only address technical and economic issues but also

ethical issues related to safety, cost-eﬀectiveness, resources, the

environment, and ecology (Naphan-Kingery et al., 2019). The

characteristics of GI engineering ethics are mainly shown in

the following three aspects. (1) The development speed of

digital technology and the transmission speed of information

are incomparable to many traditional engineering technologies.

At the same time, because the transmission speed is beyond

imagination, the spread of good in the society makes people

happy, while the spread of evil makes people unprepared (Burr

et al., 2020). As the development of engineering ethics often

lags behind the development of technology, the researchers

and educators of engineering ethics feel more pressure in the

face of a large number of emerging and rapidly spreading

new phenomena and new events today. (2) In the digital

society, more information is generated through production and

exchange, and is ﬂooded in all spaces of the society. At the same

time, people are able to record more and more information, and

even to present historical events in front of their eyes, which

further expands the concept of time and space. It is a great

challenge to carry out engineering ethics research and education

in such a wide range of ﬁelds. (3) The above two characteristics

result in the complexity and diversity of engineering ethics

research in the ﬁeld of information. This means that ethical

research and education should not only consider the impact of

engineering technology but also consider the human thought

and spirit it carries (Sorenson, 2019).

The ethical problems and characteristics of GI engineering

highlight the necessity of ethics education oriented to GI

engineering. As a part of quality education, GI engineering

ethics education (EEE) helps students to pursue the value

rationality of science and technology in future engineering

practice (Balakrishnan et al., 2021). It is responsible for the

sustainable development of human society in the future, and

deals with the relationship between engineering and humans,

society and nature objectively, fairly and impartially (Frigo et al.,

2021). This maximizes the positive impact of engineering in

promoting human safety, health, and wellbeing.

The core of EEE for GI manufacturing is to guide

students to deal with the ethical problems in engineering

independently. This requires students to establish a conscious

sense of responsibility for the overall social signiﬁcance and

long-term social impact of engineering activities, and have the

practical ability to identify, analyze, and solve new problems

in engineering ethics (Stransky et al., 2021). On the basis of

a dialogue with the public and other stakeholders, engineering

ethics is constructed to make their ethical decisions and actions

have practical eﬀects on engineering practice. At present, the

ethics education of GI engineering is in the development stage.

It is urgent to fully understand the signiﬁcance of evaluating

the development of GI EEE and to promote teaching through

evaluation. Only based on the GI manufacturing situation

system to understand the implementation status of the core

education of EEE can we objectively grasp the improvement

space of GI EEE. Therefore, it is of great theoretical and practical

signiﬁcance to establish an eﬀective evaluation index system

and assign weight to each index to evaluate the implementation

status of the core education of GI EEE.

In 1980, Hastings Center put forward a ﬁve-point consensus

on the goal of ethical education, namely, stimulating ethical

imagination, identifying ethical problems, analyzing key ethical

concepts and principles, helping students to deal with

ambiguous problems and improving the responsibility of

the educated (Avci, 2021). Many scholars analyzed the

teaching objectives of engineering ethics courses in colleges

and universities and summarized a list of EEE objectives,

including nine items (Balakrishnan et al., 2021; Frigo et al.,

2021). These contents include ethical imagination, students’

discovery of problems, students’ analysis of key ethical concepts

and principles, students’ handling of ambiguity, students’

serious treatment of ethical issues, students’ sensitivity to

ethical issues, students’ mastery of relevant ethical principles,

ethical judgments and ethical will. Newberry (2004) proposed

three categories of educational goals: emotional participation

(willingness to make ethical decisions), intellectual participation

(using ethical decision-making tools to solve ethical problems),

and specialized knowledge (being familiar with ethical concepts,

theories, and norms) (Newberry, 2004). The above contents

can be further transformed into four aspects of EEE goals. (a)

Enhanced ethical sensitivity (identiﬁcation of ethical issues);

(b) Development of ethical knowledge (understanding of

terminology, ethical codes, ethical theories); (c) Strengthening

ethical judgment (making judgments and decisions based

on sound grounds rather than chance or common sense);

(d) Enhancing ethical commitment, conﬁdence, and courage

(taking action to address ethical issues). Based on the

research results, this study fully absorbs the viewpoints of the

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Yin and Zhang 10.3389/fpsyg.2022.926133

above scholars and integrates the objectives of EEE into the

following three aspects. (1) Ethical sensitivity or awareness. The

educational objective focuses on enhancing students’ awareness

of ethical situations. It is necessary to improve the students’

sensitivity to the ethical issues they may encounter after

working in the industry, and to understand the issues related

to people, society, and nature behind engineering technology

(Maxwell et al., 2021). (2) Ethical knowledge and skills. The

educational goal focuses on helping students know how to

avoid and solve ethical problems involving honesty, fairness,

wellbeing, environmental protection, and war. This goal can be

achieved in a variety of ways, such as learning ethical principles,

ethical theories, classic cases or carrying out engineering

practice (Saada, 2022). (3) Ethical willpower. The educational

goal focuses on internalization and requires students to have

conﬁdence and courage in dealing with ethical issues. On the

basis of understanding and mastering the values of the engineer

community (such as justice and sustainable development, etc.),

the engineer should form and develop individual moral laws, be

able to judge right and wrong autonomously, and achieve the

unity of knowledge and action (Balakrishnan et al., 2020).

The teaching strategies of engineering ethics mainly include

course strategies, teaching contents, teaching methods, and

examination methods. At present, the main curriculum strategy

includes an independent curriculum, an embedded curriculum,

and integration with a non-technical curriculum (Mitcham

and Englehardt, 2019). (1) Independent courses. Independent

courses, the most common form of instructions, are usually

taught as electives by regular teachers over a full semester,

and the syllabus covers a variety of ethical topics, such as

engineering ethics and politics (Bielefeldt et al., 2018). (2)

Embedded courses. The embedded curriculum emphasizes the

introduction of ethics education in all professional courses

and the separation of knowledge points of ethics education

into diﬀerent professional courses, which is one of the main

trends of current ethics education (Grosz et al., 2019). (3)

Integration of engineering ethics and non-technical courses.

The EEE should be integrated into non-technical courses of

humanities and social sciences, especially science, technology,

and society courses to carry out EEE (Winberg et al., 2020).

In addition, the delivery strategy mainly consists of a summit

course (incorporating engineering ethics into the ﬁnal project)

and a seminar for senior students. The teaching content

of engineering ethics mainly includes the following aspects

(Haghighattalab et al., 2019; Martin et al., 2021). (1) Cases.

Common engineering ethics cases include challenger launch

failure and Bhopal chemical leakage, etc. Cases are mainly

from real historical events or ﬁctional scenarios. (2) Ethical

code. (3) Ethical dilemmas or conﬂicts of interest. (4) Ethical

theory, mainly including deontology, utilitarianism, morality

ethics, China will also teach the theory of Confucianism, Taoism,

and other schools. (5) Commonly used concepts of engineering

ethics, ethical decision-making tools, engineering and laws and

regulations (especially intellectual property rights), engineering

and sustainable development are also frequently taught. In

addition, China also attaches importance to the teaching

of craftsman spirit, excellent traditional culture, patriotism,

and model worker spirit in EEE. At present, there is no

signiﬁcant diﬀerence in the teaching methods advocated by

diﬀerent scholars.

Teaching methods include case studies, group or classroom

discussions, guided teaching, literature learning, project-

based learning, games or role playing, service learning, etc.

(Balakrishnan et al., 2020, 2021; Martin et al., 2021). In addition,

engineering ethics educators have also explored and applied

other new teaching methods. (1) Case studies. Case study is

one of the most common methods in EEE. Cases provide an

eﬀective medium for examining ethical dilemmas from multiple

perspectives and encourage students to develop action plans

based on diﬀerent ethical theories so that they can simulate

ethical decisions in a professional context as realistically as

possible. (2) Games and role playing. Through games, students

can gain a practical understanding of ethical dilemmas. In the

games, the students can fully exercise and improve the ability

of negotiation, strategic planning, speech, and so on. (3) Service

learning. At present, service learning is paid more and more

attention in EEE. This method requires students to integrate

into the real world of engineering ethics and experience and

outline the real state of engineering ethics through participating

in a community service project. In service learning, students

participate in organized and continuous service activities related

to course learning and meet speciﬁc needs of the community.

Then, the students summarize and explain the experience

through classroom discussions or diaries. Service learning is

widely used in education and teaching of many subjects, and its

popularity continues to grow.

The assessment methods of engineering ethics courses

are as follows (Hess and Fore, 2018; Hagendorﬀ, 2020). (a)

Written reports, usually completed by individuals or groups;

(b) Presentation; (c) exams/in-class tests; (d) Literature reading;

(e) Daily performance, such as attendance, online and oﬄine

interactions, group performance, study notes, etc.; (f) Creative

product production, such as the development of environmental

protection products that can eﬀectively solve the problem of

sewage discharge.

The evaluation of the eﬀect of EEE in the United States

has the following characteristics (Hess and Fore, 2018). (1)

the evaluation focuses on the ethical reasoning ability of

engineering students; (2) Formed a relatively complete and

scientiﬁc evaluation tool system for EEE; (3) Comprehensive

use of quantitative and qualitative evaluation methods; (4)

Limitations of the study design were noted; (5) Pay attention

to the diversity of the assessment subject and assessment

environment. Compared with foreign countries, China is still

in the exploratory stage in the determination of evaluation

subjects, the exploration of evaluation methods, and the

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Yin and Zhang 10.3389/fpsyg.2022.926133

formulation of evaluation tools. At present, EEE in China has

been grounded in some colleges and achieved initial results.

However, it is urgent to fully understand the signiﬁcance

of evaluation to the development of GI EEE and anchor

the actual needs of new engineering construction (Ye et al.,

2020). To explore the evaluation subjects, methods and

tools suitable for diﬀerent development stages of EEE are

beneﬁcial to promote the high-quality development of EEE in

China. At present, the eﬀect evaluation of EEE mainly uses

qualitative evaluation and quantitative evaluation (Bielefeldt

et al., 2018). (1) Quantitative evaluation. The most common

quantitative assessment methods mainly include questionnaires,

pre/post tests, student productivity, moral assessment tools,

etc. (2) Qualitative evaluation. The most common qualitative

assessment methods mainly include interviews or focus

groups and classroom observations by teachers. Therefore, a

combination of qualitative and quantitative assessment methods

can be used to assess the comprehensive level of core mechanism

of GI EEE.

Based on the existing research, it is summarized as follows.

First, the goal of EEE is clear, and it is a unity formed by the

superposition of feeling, knowing, and expressing. Second, the

teaching strategies of EEE are rich and diversiﬁed. Thirdly, the

eﬀect of EEE is aﬀected by many factors. Fourthly, evaluation

feedback is an important part of quality control and continuous

improvement in EEE. With the gradual maturity of EEE teaching

methods, EEE evaluation is regarded as an important means to

promote the in-depth development of EEE practice. Its purpose

is to investigate whether all kinds of EEE teaching models

are eﬀective and to pay attention to whether students meet

some educational goals. The evaluation object is no longer

limited to students but includes teachers, teaching materials,

teaching environment, and other categories. In recent years,

scholars have gradually attached importance to the evaluation

of the process and results of GI EEE, and emphasized the

continuous optimization of teaching programs to maintain their

eﬀectiveness. However, compared with developed countries,

there are few practical exploration and theoretical research

achievements on the evaluation of GI EEE in China.

At present, the integration of green manufacturing and

intelligent manufacturing has become the key to high-

quality economic development. The improvement space of GI

EEE can be dialectically grasped only by understanding the

implementation status of key education of EEE based on GI

manufacturing situation system. Therefore, it is very necessary

to establish an eﬀective evaluation index system and assign

weight to each index to evaluate the implementation status of

key education of GI EEE. On the one hand, the theoretical

structure of the key education of GI EEE is analyzed and

discussed. On the other hand, by constructing the evaluation

index system, the paper conducts an evaluation survey for

multiple types of personnel, and empirically evaluates the

implementation status of key education of GI EEE in colleges

and universities by using a fuzzy analytic hierarchy process and

a fuzzy comprehensive evaluation method. It is helpful to ﬁnd

the educational obstacles aﬀecting the development of GI EEE

on the basis of rational reﬂection of objective facts. This is

conducive to focusing on the key and diﬃcult points and weak

links, and observing the overall trend of generation, change, and

iteration of GI EEE.

The rest of this paper is as follows. Section Core Mechanism

and Evaluation System is a core mechanism and an evaluation

system. Fuzzy AHP and a fuzzy comprehensive evaluation

method are shown in section Methodology. Section Results and

Discussion is the results of fuzzy comprehensive evaluation of

three layers. Conclusions and future prospects are presented in

section Conclusions.

Core mechanism and evaluation

system

GI EEE

The ethical education of GI engineering is inﬂuenced

by many factors (intelligent engineering characteristics,

educational consensus, carbon reduction, and eﬃciency

increase) (Holsapple et al., 2012; Ngoepe et al., 2022).

Situational education, collaborative education, and cultivation

education play their roles from diﬀerent angles. Finally, the

eﬀect is to enhance the quality of college education supply

and gather the synergy of ethical education. If the above three

kinds of education are in a positive state, it will make EEE

run smoothly and orderly, and ethics teaching can also better

enter the ear, the brain, and the heart. In order to further

explore the inner relationship of the key education of GI EEE,

the conditional matrix tool was used to analyze situational

education, collaborative education, and cultivation education.

The conditional matrix divides elements such as conditions and

consequences into many levels from micro to macro, including:

(1) Action; (2) Interaction; (3) Collective; (4) Secondary

organization; (5) Organization and system; (6) Community; (7)

Country; (8) International.

As shown in Figure 1, the key education of GI EEE includes:

cultivation education of micro dimension, coordination

education of medium dimension, and situational education of

macro dimension.

(1) Cultivation and education. In training and education,

educators transform the GI ethical concepts based on the

country, society, and class to the educated by means of

certain means and methods. To meet the requirements of GI

engineering ethical literacy, training education is an inevitable

measure of GI EEE (Zhang et al., 2022). Training education

is an inevitable measure of GI EEE. The cultivation education

responds to the problem of how to enhance the supply quality of

the educational end of colleges and universities through GI EEE.

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Yin and Zhang 10.3389/fpsyg.2022.926133

FIGURE 1

A core mechanism of GI EEE.

The practice of GI EEE has not yet eﬀectively responded to the

core knowledge system, teaching syllabus, education methods,

and other key issues.

(2) Collaborative education. Collaborative education is that

all intermediary objects participating in GI EEE activities

are combined into an orderly structure and function whole

under the guidance and restriction of certain rules, so as

to strengthen the practical eﬃciency of EEE. Collaborative

education is the objective choice of GI EEE. Collaborative

education responds to the question of how to gather the

collaborative power of ethical education in GI EEE (Young et al.,

2021). At present, the concept of collaborative development

has become an important proposition for the development of

higher education. In the collaborative perspective, education

emphasizes the interactive connection among all elements,

especially the dynamic resource sharing, and realizes the

common development goals by means of the optimal allocation

of information and resources. The development of GI EEE

needs the synergistic eﬀect of all intermediary objects, and

cannot only rely on the initiative of universities. GI EEE is a

complex and systematic project, which needs to give play to

the leading role of the government and strengthen the incentive

and support of the system. This also needs to give play to

the advantages of enterprise organizations, promote school-

enterprise cooperation, and encourage enterprises to take the

initiative to undertake the responsibility of EEE for employees

(Kim, 2022).

(3) Situational education. The comprehensive description of

GI EEE cannot be separated from the systematic interpretation

of situational education. The ethics education of GI engineering

in developed countries has diﬀerent characteristics. Can the

region copy the experience of other countries? How to do the

local ethics education of GI engineering well? The answers to

the above problems lie in the ﬁelds constructed by situational

education, including social factors, natural factors, and spiritual

factors (Zhang and Zhu, 2021). Situational education is an

inevitable requirement of GI EEE. The ﬁeld of GI EEE integrates

the needs and value judgment of things, and reﬂects the setting

of identity and position of things, the choice of educational

topics and contents, and the trend of teaching practice. The

correct choice of each region is not only to introduce and learn

the GI EEE from developed countries but also to highlight the

nationality and identify the heterogeneous characteristics that

cannot be perfectly explained by the GI EEE theory (Maqsoom

et al., 2020). In addition, each region should try to develop

regional characteristics of EEE, and ﬁnally put forward the local

EEE program.

Evaluation system of implementation

eect

GI EEE is the product of the unity, integration, inﬂuence,

and deployment of various elements in key education, and

also the result of the comprehensive eﬀects of training

education, collaborative education, and situational education.

Corresponding indexes are selected from the three dimensions

of cultivation education, collaborative education, and situational

education to form the element community of evaluation indexes.

To ensure the scientiﬁc nature and accuracy of index selection,

Harbin Engineering University, Yanshan University, long-term

commitment to EEE, engineering education and engineering

ethics research of three GI engineering ethics teachers, education

economics and management professors, and three science and

technology philosophy and ethics professors was invited to

set an index and the statement for further revision. Through

repeated communication, 3 ﬁrst-level indicators, 8 second-level

indicators, and 27 third-level indicators were ﬁnally determined.

Evaluation system of implementation eﬀect is shown in Table 1.

(1) Training and education. Teachers’ personal experience,

professional experience, and belief dynamics aﬀect teachers’

identity, and then aﬀect teachers’ eﬀectiveness and practice

in the classroom. Teachers’ teaching is inﬂuenced by many

factors from inside and outside the classroom, such as

professional knowledge, subject background, and teaching

content (Lomask et al., 2018). Teachers’ Colleges (departments),

gender, teaching years, practical experience of GI engineering,

and adopted curriculum delivery strategies (the independent

curriculum, the embedded curriculum, integration with the

non-technical curriculum, the vertex curriculum, etc.) all have

an impact on teaching eﬀectiveness. The strength of teachers

not only determines the quality of GI engineering ethics

teaching but also determines whether the concept of GI

EEE can be implemented This mainly includes the number

of GI engineering ethics teachers and the interdisciplinary

teacher team. Strengthening interdisciplinary cooperation with

humanities and social sciences departments is an important

link for engineering departments to deeply understand and

grasp the speciﬁc ethical issues in the ﬁeld of GI engineering.

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TABLE 1 Evaluation system of implementation eect.

Main criterion

layer

Sub-criteria

layer

Scheme layer serial Number Interpretative statement

Training and

education A

Faculty A1 Teachers’ professional level

and ability A11

1 The professional level and ability of teachers are competent for

the work of GI ethics education

Number and structure of

teaching staﬀ A12

2 Building a reasonably structured teaching staﬀ

Teacher career development

opportunities A13

3 Teachers can get full career development opportunities

Teaching process

Course material A21 4 It can play a basic role in guiding the direction of GI EEE and

ensuring the high-quality development of education.

Teaching case A22 5 It can provide rich and diverse case choices for relevant teachers

to better support the realization of the goal of GI EEE.

Practice resource A23 6 It can bring students real ethical problems of GI engineering and

improve their ability to deal with ethical problems of GI

engineering.

Teaching quality A3 Combination of theory and

practice A31

7 It can help students understand the ethical issues of GI

engineering and the ethical norms and norms of GI engineering

Full cycle ethics education

A32

8 Organically run through professional courses, graduation design

and other links

Course evaluation and

improvement A33

9 Timely evaluate and improve the GI engineering ethics course

Collaborative

education B

Government B1 Supporting system

construction B11

10 The government actively promotes the construction of

supporting system for ethics education of GI engineering

Attention at the policy level

B12

11 The government emphasizes the value of GI EEE from the

perspective of policy

Funding level B13 12 Various research topics or educational reform projects have fully

supported the research and practice of GI EEE

Enterprise

organization B2

Attach importance to talent

ethical literacy B21

13 Enterprises (especially engineering enterprises) pay full attention

to the green intelligence and ethical literacy of talents

Participation form and quality

B22

14 Schools and enterprises jointly formulate the training objectives

and training programs of GI EEE, jointly develop ethics courses,

and provide teaching practice resources

Willingness to participate B23 15 Enterprises have a strong willingness to participate in GI EEE

Situational

education C

International vision

Grasp of Global Frontier

Situation C11

16 Learn and introduce foreign advanced experience and teaching

resources

Academic dialogue C12 17 Published research articles on GI EEE in foreign journals

Practice dialogue C13 18 Cooperate with foreign universities to set up GI ethics courses

Regional discourse

Theoretical localization C21 19 Constructing educational theory in line with national conditions

and the characteristics of GI engineering

Practice localization C22 20 Combined with the speciﬁc situation, carry out GI ethics

innovation on the educational concept, orientation, content and

methods

Local demands capture C23 21 In the process of carrying out GI EEE, the regional cultural

background, social system and engineering practice have been

given full attention

There is a shortage of teachers who understand GI engineering

and can teach ethics courses, which requires inviting part-time

teachers from government, enterprises, and other institutions to

teach as much as possible. Funding resources from universities

and governments are an important support to continuously

promote teachers’ innovative educational practice. Teachers’

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career development opportunities also include the adequacy

of teachers’ access to teacher training opportunities (Lawlor,

2021). GI EEE must strengthen the construction of teaching

resources of GI EEE. GI EEE provides suﬃcient education and

teaching resources, including curriculum materials, teaching

cases, and practice bases. The construction of teaching materials

for GI engineering ethics needs to be based on scientiﬁc

research. The construction of the GI engineering ethics case

base is a comprehensive work. The reason why an advanced

learning concept is suitable for GI EEE is that the thinking

characteristics of diﬀerent cognitive development stages are

diﬀerent. In educational practice, the corresponding educational

focus and operation strategies should also be diﬀerent at each

stage; appropriate measures are taken to complete key tasks,

which can make the educational eﬀect work with half the eﬀort

(Lavi and Dori, 2019).

(2) Collaborative education. For the collaborative goals

of GI EEE, on the one hand, the all-round development

of GI EEE should be actively supported. This is not only

conducive to promoting the multi-angle integration and sharing

of educational resources but also can form a huge resource

supply source. On the other hand, it is necessary to promote

the independent development of multiple governance subjects,

including the government and enterprises and enhance the

consciousness of conscious participation (Borenstein et al.,

2019). The gradual development of long-term interests involves

the foundation for independent development and enhancing

communication and interaction. On the basis of mutual beneﬁts

and win-win, the collaborative promotion of GI EEE should

be carried out. In terms of promoting goals, we should

focus on realizing strategic coordination and construction

coordination, and take co-construction and sharing as the

core content. In the standardization of teaching system, GI

engineering ethics, joint construction of university teaching

resources, teacher training, and other aspects of cooperation

and exchange of needs. The normalization and standardization

of multi-subject participation are important foundations in the

overall development of GI EEE. According to the theory of

synergy advantage, member structure is dynamic. At present,

enterprises have not been deeply involved in the collaborative

development of GI EEE, and the auxiliary characteristics at

the edge are obvious (Vveinhardt et al., 2019). However,

as the main employment channel for engineering students,

enterprises play a special role in the education of GI engineering

ethics. In order to realize the organic combination of GI

engineering ethics and career, some colleges and universities

began to explore ways to cooperate with enterprises to

carry out GI EEE. Enterprises are becoming an important

part of the coordinated development of GI EEE. Some

universities use school-enterprise cooperation platforms to let

students go deep into the grassroots of enterprises (Zhang

and Zhu, 2021). Students communicate with GI engineers

of enterprises, and constantly strengthen the ethics of GI

engineering in the process of verifying and correcting their

own career.

(3) Situational education. Modern GI engineering projects

not only involve technical and economic issues but also are

related to sustainability, security, cost eﬀectiveness, resources,

ecological environment, and other issues (Polmear et al., 2021).

The impact of automation and robot technology on human

psychology, the impact of information technology on human

society, and the threat of genetic engineering on human

dignity are becoming new topics in the ethical education of GI

engineering. GI EEE in China is undergoing a process from

transplantation, imitation, veriﬁcation to transformation and

integration (Clancy, 2021). However, the duration is relatively

short, and the real absorption and internalization are less.

The depth and breadth should be expanded. Therefore, system

understanding and system integration of system construction

are very lacking. To give full play to the energy and function of

GI EEE, it should be endowed with the ability of self-perfection

and development. The localization of ethical education system

construction of GI engineering focuses on two ways. First,

the local transformation of professionalism has gradually

established the GI engineering ethical standard system and

the GI engineering vocational system construction theory

under the guidance of discourse. The acceptance of GI EEE

assessment will be a necessary prerequisite for the certiﬁcation of

professional engineers in the future. Secondly, the certiﬁcation

of GI engineering education based on real needs should clarify

the requirements that engineering graduates must possess the

ethical literacy of GI engineering.

Methodology

Determination of assessment methods

In the study, the evaluation system of implementation

eﬀect includes the main criterion layer, the sub-criteria layer,

and the scheme layer serial. The evaluation of GI EEE

implementation status is a typical multi-factor comprehensive

evaluation problem. Most of the indicators are qualitative

indicators with fuzzy characteristics, which are diﬃcult to

accurately judge and grade. The fuzzy comprehensive evaluation

method is a kind of comprehensive evaluation method

based on fuzzy mathematics. The comprehensive evaluation

method transforms qualitative evaluation into quantitative

evaluation according to the membership degree theory of fuzzy

mathematics. Fuzzy mathematics makes an overall evaluation

of things or objects restricted by many factors. It has the

characteristics of clear results and strong systematicness, and

can solve fuzzy and diﬃcult to quantify problems well. Analytic

hierarchy process (AHP) can clearly show the attribute weight

of indicators at each level (Yin et al., 2022b). The weight is

determined by experts based on triangular fuzzy numbers. The

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advantages of triangular fuzzy numbers are as follows: First,

the expert team is very familiar with the ﬁeld of triangulated

fuzzy books. Secondly, the triangle fuzzy number can solve

the contradiction that the performance of the evaluated object

cannot be measured accurately but can only be evaluated

by natural language. AHP is an eﬀective tool to deal with

complex decision problems. In view of the uncertainty caused

by expert subjective judgment and language fuzziness in the use

of AHP, the traditional nine-level scale method is combined with

triangular fuzzy number. In the process of using the fuzzy AHP

method, the step of the consistency test can be omitted (Buckley,

1985; Akkaya et al., 2015). Therefore, the fuzzy AHP method

and the fuzzy comprehensive evaluation method based on the

triangular fuzzy number were used to assess the comprehensive

level of the core mechanism of GI EEE.

Fuzzy AHP

Based on this combination, the established nine-level fuzzy

scale is shown in Table 2.

Table 2 was used to collect experts’ judgment opinions on

the relative importance of each factor. Assuming that there

are nrisk factors in an indicator layer, and ˜a(k)

ij is the relative

importance of the ifactor judged by the kexpert to the jfactor,

the fuzzy judgment matrix Ã(k)of this indicator layer is shown

in Formula (1).

Ã(k)=˜a(k)

ij =







(1, 1, 1) l(k)

12 ,m(k)

12 ,u(k)

12 ··· l(k)

1n,m(k)

1n,u(k)

1n

1/u(k)

12 , 1/m(k)

12 , 1/l(k)

12 (1, 1, 1) ... l(k)

2n,m(k)

2n,u(k)

2n

1/u(k)

1n, 1/m(k)

1n, 1/l(k)

1n 1/u(k)

2n, 1/m(k)

2n, 1/l(k)

2n··· (1, 1, 1)







k=1, 2, ··· ,K,i,j=1, 2, ··· ,n

(1)

Then the following modiﬁed formula is used to calculate

the triangular fuzzy number of the weight of risk factors at

each level.

S(k)

i=





j=1

l(k)

j=1

l(k)

ij +

z=1,z6=i

j=1

u(k)

j=1

m(k)

z=1

j=1

m(k)

,u(k)

j=1

u(k)

ij +

z=1,z6=i

j=1

l(k)







i,j,z=1, 2, ··· ,n,k=1, 2, ··· ,K

(2)

S(k)

irepresents the triangular fuzzy number of the single

ranking weight of the irisk factor judged by the kexpert.

It is assumed that the index layer under the target layer is the

ﬁrst layer, and each sub-index layer is the second ∼(n-1) layer

in turn. Then, the total ranking weight relative to the target layer

obtained by iterative calculation of single ranking weight of each

factor can be expressed as:

h(k)

i=5n−1

m=1S(k)(m)

i,k=1, 2, ···K,i=1, 2, ···n(3)

S(k)(m)

iis the weight of the m-level index judged by the k

expert, and h(k)

iis the total ranking weight of the bottom index

to the target level.

In order to facilitate sorting and comprehensive weight

calculation, the results are defuzziﬁed. Given a triangle fuzzy

number ˜

N=(l,m,u), the defuzziﬁcation value of the triangle

fuzzy number can be calculated by using Formula (4).

Ndefuzziﬁcation =l+2m+u

4(4)

Fuzzy comprehensive evaluation method

The fuzzy comprehensive evaluation method is a kind of

comprehensive evaluation method based on fuzzy mathematics.

The fuzzy relation synthesis principle is used to quantify the

factors that are relatively fuzzy and diﬃcult to be quantiﬁed and

express them with accurate mathematics. Fuzzy comprehensive

evaluation is characterized by strong systematicness and clear

results. It is mainly used for comprehensive evaluation of

objects aﬀected by multidimensional factors and unstructured

or diﬃcult to quantify problems. The data in this study are

all qualitative data, and have the characteristics of multilevel,

multi-factor, and fuzziness. Therefore, the fuzzy comprehensive

evaluation method is suitable for this study. The fuzzy

comprehensive evaluation method is used to evaluate the

implementation status of GI EEE. The speciﬁc analysis steps are

as follows.

(1) Determine the evaluation object factor Set Uand

evaluation Set V. At the same time, determine the weight of each

inﬂuencing Factor W.

(2) Establish the scoring membership function and

comprehensive evaluation Matrix Rof each factor, calculate the

membership Degree R, and obtain the fuzzy set.

(3) The fuzzy comprehensive evaluation Set Y=W×Ris

calculated by the comprehensive evaluation matrix R.

(4) Calculate the comprehensive evaluation score S=W×N

of the evaluation object with the measurement Scale N.

Data source

In order to obtain index weight, constructing judgment

matrix is the key point of fuzzy AHP. According to the

evaluation criteria in Table 2, 35 experts (teachers, managers,

and scholars) in this study were given the empowerment table

of evaluation indicators of GI EEE through email and an

oﬄine interview. The experts were asked to rate the relevant

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TABLE 2 Expert judgment term and fuzzy number transformation relationship.

Scale Meaning Triangular fuzzy number Reciprocal

1 The two elements are equally important (1,1,1) (1,1,1)

2 Between equally important and slightly important (1,2,3) (1/3,1/2,1)

3 The former is slightly more important than the latter (2,3,4) (1/4,1/3,1/2)

4 Between slightly important and more important (3,4,5) (1/5,1/4,1/3)

5 The former is stronger and more important than the latter (4,5,6) (1/6,1/5,1/4)

6 Between strong importance and strong importance (5,6,7) (1/7,1/6,1/5)

7 The former is more important than the latter (6,7,8) (1/8,1/7,1/6)

8 Between strong importance and extreme importance (7,8,9) (1/9,1/8,1/7)

9 The former is more important than the latter (8,9,9) (1/9,1/9,1/8)

indicators based on their own research or experience. In order

to obtain the original data of this study, the formal survey of this

questionnaire selected the research objects that met the research

conditions. From September to December 2021, questionnaires

were distributed and collected by star in Hebei province,

with 219 valid samples collected in total. Consistent with the

pre-survey, the sample of this survey is still dominated by men

(51.36%). Most of the respondents (86.49%) were teachers.

Engineering was the highest discipline (specialty) background

of the respondents (46.22%), of which digital engineering

accounted for 54.36; green engineering accounted for 36.89. The

teaching period is mainly 1–3 years and 4–6 years, accounting

for 62.86%, which is related to the short development time of GI

EEE in China. Approximately, 82.36% of the respondents have

participated in teacher training, which beneﬁted from the great

attention and promotion of teacher training by the Steering

Committee for Graduate Education of Engineering Specialty

in recent years. Universities (66.89%) and enterprises (12.34%)

were the main channels for the respondents to obtain ﬁnancial

aid, while only 7.63% of the respondents obtained ﬁnancial aid

from the government (such as educational reform projects of

the education department). Only 53.96% of the respondents

have carried out interdisciplinary cooperation, among

which 36.94 and 28.74% have carried out interdisciplinary

cooperation in education and teaching and academic

research, respectively. The expertise of the respondents is

very uniform.

Results and discussion

Determination of weight based on fuzzy

AHP

According to the evaluation criteria in Table 2, more than

35 experts judged the relative importance of 21 indicators. Take

the judgment results of A11–A13 indicators as an example to

TABLE 3 Judgment on the importance of each risk factor under

meteorological conditions.

C1A11 A12 A13

Expert 1 A11 (1,1,1) (1/4,1/3,1/2) (1/9,1/8,1/7)

A12 (2,3,4) (1,1,1) (1/3,1/2,1)

A13 (7,8,9) (1,2,3) (1,1,1)

illustrate the calculation. The judgment results of Expert 1 are

shown in Table 3.

According to Table 3, the fuzzy judgment matrix of this level

is constructed as follows:

A(1)

C1=





(1, 1, 1) (1/4, 1/3, 1/2) (1/9, 1/8, 1/7)

(2, 3, 4) (1, 1, 1) (1/3, 1/2, 1)

(7, 8, 9) (1, 2, 3) (1, 1, 1)





(5)

Formula (2) was used to calculate the

fuzzy judgment matrix ˜

A(1)

C1, and the triangular

fuzzy number of the indicator weight vector is

as follows:

S(1)

C1=





S(1)





=





0.0668 0.0860 0.1175

0.1854 0.2654 0.3667

0.5408 0.6486 0.7347





(6)

Finally, formula (3) is used to calculate the triangular

fuzzy value of each index weight, and the mean value is

taken for the judgment results of each expert. Then, Formula

(4) is used to defuzzify the results. This round of weight

survey recovered a total of 35 scoring tables. In this study,

the software Yaahp was used for auxiliary calculation, and the

consistency test of each expert’s grading table was carried out

one by one. The arithmetic mean was used to calculate the

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TABLE 4 Weight of evaluation index of GI EEE.

Main criterion layer Weight 1 Sub-criteria layer Weight 2 Weight 3 Scheme layer Weight 4 Weight 5

A 0.389 A1 0.304 0.118 A11 0.387 0.046

A12 0.264 0.031

A13 0.349 0.041

A2 0.367 0.143 A21 0.291 0.042

A22 0.312 0.045

A23 0.397 0.057

A3 0.329 0.128 A31 0.291 0.037

A32 0.396 0.051

A33 0.313 0.040

B 0.324 B1 0.428 0.139 B11 0.334 0.046

B12 0.329 0.046

B13 0.337 0.047

B2 0.572 0.185 B21 0.296 0.055

B22 0.383 0.071

B23 0.321 0.059

C 0.287 C1 0.396 0.114 C11 0.326 0.037

C12 0.347 0.039

C13 0.327 0.037

C2 0.604 0.173 C21 0.262 0.045

C22 0.426 0.074

C23 0.312 0.054

weight of the score integration of 35 experts. The ﬁnal weight

calculation results of evaluation indicators at all levels are shown

in Table 4.

According to Table 4, the relative weights of training

education, collaborative education, and situational education

in the main criteria layer to the target layer are 38.9, 32.4,

and 28.7%, respectively. It can be seen that the evaluation

dimension with the highest weight is training education. At

present, the most important education is to strengthen the

ethics education of GI engineering in the process of cultivating

college students. The coordination of GI EEE is becoming

more and more important. Integration and co construction are

important pursuits of GI EEE. Integration refers to the deep

integration of GI EEE with economy and society; engineering

practice and talent training EEE should be of real value to man,

nature, and society. Joint construction is to expand education

supply through multiple channels. The joint participation of

multiple subjects provides a qualitative opportunity to promote

the connotative and structured development of GI EEE. GI EEE

is concrete and diverse. In the process of practice, each country

not only has interoperability, but also the actual situation

is very diﬀerent. How to correctly grasp the development

situation of international GI EEE and accurately implement

policies according to the practical development trend of GI

EEE has a profound impact on the eﬀectiveness of regional

GI EEE.

The results of fuzzy comprehensive

evaluation of sub-criterion layer

(1) Construct the factor set. According to Table 2, the factor

set of fuzzy comprehensive evaluation of the sub-criterion layer

is γ={γ1, γ2, γ3}, where γ∈{A1, A2, A3, B1, B2, C1, C2}.

(2) Construct the comments set of the sub-criteria layer.

This study divides the reality of the key mechanism of industrial

process ethics education into ﬁve grades as follows: V=

{V1, V2, V3, V4, V5}A. This means very poor, poor, average,

good, and very good.

(3) Construct the sub criteria layer weight set. According

to the weights of evaluation indicators at all levels, this study

constructs the weight set vectors of indicators, which are

as follows:

WA1= {0.387, 0.264, 0.349}; WA2= {0.291, 0.312, 0.397};

WA3= {0.291, 0.396, 0.313}; WB1= {0.334, 0.329, 0.337};

WB2= {0.296, 0.383, 0.321}; WC1= {0.326, 0.347, 0.327};

WC2= {0.262, 0.426, 0.312}

The original data evaluation matrix is shown in Table 5.

According to Table 5, the A1 membership matrix of key

education status evaluation of GI EEE is constructed as follows:

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TABLE 5 The original data evaluation matrix.

Main criterion layer Sub-criteria layer Scheme layer V1 V2 V3 V4 V5 Mean value

A A1 A11 6 36 114 52 11 3.1187

A12 4 35 122 49 9 3.1096

A13 9 32 98 63 17 3.2146

A2 A21 15 32 109 55 8 3.0411

A22 19 41 104 34 21 2.9863

A23 8 31 113 55 12 3.1461

A3 A31 10 34 105 61 9 3.1142

A32 11 36 114 47 11 3.0502

A33 13 30 109 57 10 3.0959

B B1 B11 15 29 124 38 13 3.0228

B12 14 34 121 40 10 2.9909

B13 12 33 107 58 9 3.0868

B2 B21 8 26 122 55 8 3.1324

B22 7 28 116 53 15 3.1872

B23 9 24 113 60 13 3.2009

C C1 C11 15 33 120 42 9 2.9863

C12 14 31 117 47 10 3.0365

C13 5 29 128 46 11 3.1324

C2 C21 11 38 123 38 9 2.9817

C22 12 31 126 42 8 3.0137

C23 14 29 118 48 10 3.0502

RA1=





0.0274 0.1644 0.5205 0.2374 0.0502

0.0183 0.1598 0.5571 0.2237 0.0411

0.0411 0.1461 0.4475 0.2877 0.0776







The compound operation results of fuzzy matrix are

as follows:

YA1=WA1∗RA1

= {0.387, 0.264, 0.349}∗







0.0274 0.1644 0.5205 0.2374 0.0502

0.0183 0.1598 0.5571 0.2237 0.0411

0.0411 0.1461 0.4475 0.2877 0.0776







=n0.0298 0.1568 0.5047 0.2514 0.0574 o

Similarly, fuzzy comprehensive evaluation results of other

INDICATORS A2, A3, B1, B2, C1, and C2 can be obtained

as follows:

YA2=n0.0622 0.1554 0.4981 0.2258 0.0586 o

YA3=n0.0516 0.1513 0.4967 0.2553 0.0451 o

YB1=n0.0625 0.1448 0.5355 0.2078 0.0494 o

YB2=n0.0369 0.1179 0.5355 0.2567 0.0529 o

YC1=n0.0514 0.1419 0.5571 0.2042 0.0455 o

YC2=n0.0562 0.1507 0.5573 0.1943 0.0415 o

According to the maximum membership degree principle,

the maximum membership degree values in the sub-criterion

layer are all general, wherein the maximum membership degrees

of A1, A2, A3, B1, B2, C1, and C2 are 0.5047, 0.4981, 0.4967,

0.5355, 0.5355, 0.5571, and 0.5573, respectively. Therefore, it is

judged that the performance of GI EEE in this area is at the

average level.

The results of fuzzy comprehensive

evaluation of main criterion layer

According to the membership evaluation results of A1, A2,

A3, B1, B2, C1, and C2 obtained above, the fuzzy comprehensive

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Yin and Zhang 10.3389/fpsyg.2022.926133

evaluation value of the winner criterion layer can be obtained.

The evaluation results of the membership degree of A=

{A1, A2, A3}training education are as follows:

YA =WA ∗RA

=n0.304 0.367 0.329 o∗







0.0298 0.1568 0.5047 0.2514 0.0574

0.0622 0.1554 0.4981 0.2258 0.0586

0.0516 0.1513 0.4967 0.2553 0.0451







=n0.0489 0.1545 0.4996 0.2433 0.0538 o

YB =WB ∗RB

=n0.428 0.572 o∗"0.0625 0.1448 0.5355 0.2078 0.0494

0.0369 0.1179 0.5355 0.2567 0.0529 #

=n0.0479 0.1294 0.5355 0.2358 0.0514 o

YC =WC ∗RC

=n0.396 0.604 o∗"0.0514 0.1419 0.5571 0.2042 0.0455

0.0562 0.1507 0.5573 0.1943 0.0415 #

=n0.0543 0.1472 0.5572 0.1982 0.0431 o

According to the principle of maximum degree of

membership, the maximum degree of membership in the main

criterion layer is general, among which the maximum degrees

of membership of A, B, and C are 0.4996, 0.5355, and 0.5572,

respectively. Therefore, it is judged that the development of GI

EEE in this region is at the general level in terms of cultivation

education, collaborative education, and situational education.

The results of fuzzy comprehensive

evaluation of target layer

According to G= {A,B,C}obtained above, the ﬁnal

evaluation results of the membership degree of the target layer

can be obtained as follows:

YG =WG ∗RG

=0.389 0.324 0.287∗



0.0489 0.1545 0.4996 0.2433 0.0538

0.0479 0.1294 0.5355 0.2358 0.0514

0.0543 0.1472 0.5572 0.1982 0.0431 



=0.0543 0.1472 0.5278 0.2279 0.0500 

According to the principle of maximum membership degree,

the membership degree of general was the highest (0.5278).

Therefore, it is judged that the implementation status of the key

education of GI EEE in Hebei is at the general level.

Discussion

From the above research, it can be seen that the maximum

membership degree of the implementation status of key

education in GI EEE is average. In order to further distinguish

the performance diﬀerences of each indicator, this study tries

to calculate the individual and overall scores of each indicator,

which is conducive to reﬂect the evaluation results to the

maximum and more truly and eﬀectively. In this study, each

evaluation element of the comment set is assigned, with a value

of 50, 60, 70, 80, and 90, respectively. Thus, the semantic scale of

subjective evaluation is quantiﬁed. Table 6 shows the evaluation

scores of various indicators on the implementation status of key

education of GI EEE after analyzing the scoring data of 219

experts. A score of 80 or above is considered excellent, 70–80

is considered good, 60–70 is considered acceptable, and below

60 is considered unqualiﬁed.

Based on the statistical empirical test, this paper makes

a factual judgment on the implementation status of the key

mechanism of GI EEE in Hebei province.

(1) Analysis of key education. According to the score of

evaluation index, the implementation status of key education

of GI EEE is good and inferior. It is only 0.8368 higher than

the good baseline, and there is still room for improvement.

Among them, cultivation education (70.9699) and collaborative

education (71.1482) are above the average level, while situational

education (70.3050) is relatively low. According to the weight

distribution of evaluation indexes, cultivation education has the

highest weight and the most prominent importance. The second

is collaborative education and cultivation education, but the

evaluation results are contrary to them. It shows that there is a

gap between theory and practice in GI EEE. The main reason lies

in the late start of GI EEE. For a long period of time, the focus of

discussion is how to do the university itself, and the attention to

localization and coordination issues is obviously insuﬃcient.

(2) Analysis of key situations. In terms of situational

education, there was no signiﬁcant diﬀerence in the scores

of GI EEE in an international perspective (70.5151) and

regional discourse (70.1672). The key direction of promoting the

internationalization of GI EEE from an international perspective

is to actively carry out educational dialogues, including grasping

the global frontier situation and theory localization. From the

perspective of reality, the process of localization of GI EEE

needs a long time to accumulate and cannot be accomplished

overnight. Only step by step and in accordance with the world’s

leading trends can we eﬀectively improve the local meaning of

GI EEE.

(3) Analysis of key directions. Combined with the score

data of each index of the sub-criteria layer and the program

layer, the score of each index of the subordinate of collaborative

education is basically good. Among them, the score of attaching

importance to policy was the lowest, only 69.9087. This reﬂects

that the government’s willingness to participate in the ethical

Frontiers in Psychology 13 frontiersin.org

Yin and Zhang 10.3389/fpsyg.2022.926133

TABLE 6 The scores of various indicators on the implementation status of key education of GI EEE.

Target layer Sub-criteria

layer

Score Sub-criteria layer Score Scheme layer serial Score

Implementation status of

key education in GI EEE

(Score: 70.8368)

Training and

education A

70.9699 Faculty A1 71.4978 Teachers’ professional level and

ability A11

71.1872

Number and structure of teaching

staﬀ A12

71.0959

Teacher career development

opportunities A13

72.1461

Teaching process A2 70.6570* Course material A21 70.4110*

Teaching case A22 69.8630*

Practice resource A23 71.4612

Teaching quality A3 70.8313 Combination of theory and

practice A31

71.1416

Full cycle ethics education A32 70.5023*

Course evaluation and

improvement A33

70.9589

Collaborative

education B

71.1482 Government B1 70.3386* Supporting system construction

B11

70.2283*

Attention at the policy level B12 69.9087*

Funding level B13 70.8676

Enterprise organization

71.7539 Attach importance to talent ethical

literacy B21

71.3242

Participation form and quality B22 71.8721

Willingness to participate B23 72.0091

Situational

education C

70.3050* International vision C1 70.5151* Grasp of Global Frontier Situation

C11

69.8630*

Academic dialogue C12 70.3653*

Practice dialogue C13 71.3242

Regional discourse C2 70.1672* Theoretical localization C21 69.8174*

Practice localization C22 70.1370*

Local demands capture C23 70.5023*

Mean value 70.8077 Mean value 70.8230 Mean value 70.8089

*means below average.

education of GI engineering is not enough, especially the

ethical literacy of talents has not been widely valued by the

government. It is not conducive to spreading a belief in the

usefulness of ethics throughout society. On the whole, there are

still some barriers in the coordination of GI EEE. No matter

the engineering government, enterprises, college teachers, and

engineering students, all have a non-committal attitude toward

the implementation of GI EEE.

(4) Analysis of key points. In the strategy of GI EEE, the

educational idea is the cornerstone, the teaching is the diﬃculty,

and the teaching staﬀ is the support. Returning to the level

of colleges and universities, the overall performance of the

indicators of training and education subordinates is better.

The teaching staﬀ (71.4978), teaching process (70.6570), and

teaching quality (70.8313) in the sub-criteria were all at a good

level. According to the score of the program level, the optimized

space of training education includes teaching cases, full-cycle

ethics education, etc.

In view of the above analysis, the following countermeasures

are put forward.

(1) The dynamic balance of training and education on

the connection between supply and demand. The cultivation

education in the key education of GI EEE is generated from the

mutual construction of educational ideas, course teaching, and

teachers. Therefore, the pursuit of high-quality development of

GI EEE should also closely focus on the above three factors.

According to the practical needs, the vitality and activity of GI

EEE should be continuously enhanced. This needs to strengthen

the eﬀect of educational reform and innovation, anchor the

basic aspect of the educational concept, pay attention to the

basic points of teaching, and optimize the supporting line of

the teaching staﬀ. It is helpful to improve the quality and the

Frontiers in Psychology 14 frontiersin.org

Yin and Zhang 10.3389/fpsyg.2022.926133

level of GI EEE. The idea is the forerunner of action, the

deviation between educational ideas and practice orientation

will directly aﬀect the eﬀect of education. Only when the

rational understanding of EEE is in place, the action can be

targeted. The educational idea of GI engineering ethics is to

adapt to the forefront changes of engineering ethics teaching

and learning. This should follow the law of students’ cognitive

development, growth and talent, teaching and educating, with

student-centered, interdisciplinary, and collaborative education

in the ﬁrst place. The practice orientation of GI EEE and

even the whole higher education emphasizes that the roles and

functions of teachers and students should be changed positively

in educational practice. As a cross-body of multi-disciplines,

GI engineering ethics needs to guide the development of

GI EEE from the cross-disciplinary perspective. In terms

of horizontal interconnection, orderly inﬁltration of various

elements, integration and mobilization of multi-subjects and

all-round coordinated participation should be realized. This

can build a good development platform for the high-quality

development of GI EEE. GI EEE is expanded to practice, and the

theory and practice are mapped to each other. Alumni engineers

participate in classes and give full play to their advantages of

mentoring and form good positive feedback in students’ minds.

The ability and accomplishment of full-time teachers can be

improved by opening open classes, guiding young teachers

by key teachers, and establishing engineering ethics teacher

development centers.

(2) The governance means of GI EEE. Construct the

collaborative system of GI EEE and actively integrate various

forces. This is conducive to promoting the transformation

of colleges and universities from ﬁghting alone to multi-

subject coordinated development, and fully fermenting the

power of high-quality development. A reasonable and eﬀective

system supply criterion is the value dimension to maintain

and promote the coordinated and sustainable development

of GI EEE. In terms of policy, the urgency and signiﬁcance

of GI EEE should be realized from the inevitable trend of

the development of global EEE. The ethics education of GI

engineering is a complex system engineering involving a wide

range and far-reaching inﬂuence. It plays an important role in

the improvement of higher engineering education system and

the development of engineering practice in China. At the same

time, the complexity and global nature of modern engineering

make the responsibilities of engineering talents more extensive

than ever before. Engineering students need to be prepared

for their future career development. Funding resources and

teaching practice resources are the two most urgently needed

resources for GI EEE. The development of GI EEE is highly

dependent on scientiﬁc research and teaching funds from the

government, forming an engineering ethics funding system

dominated by educational reform projects, supplemented by

self-science and social science funds. In this way, a number

of high-level scientiﬁc research projects and achievements can

be generated and turned into GI EEE and teaching resources.

The key reason for the long-term disconnection between the

theoretical teaching and practical teaching of GI EEE is the

lack of practical resources. Universities and enterprises should

make full use of the existing industry-university cooperation

platform, oﬀ-campus cooperative practice teaching bases, oﬀ-

campus practice training sites, etc., to move the practice teaching

link of engineering ethics to the practice site of enterprises.

This will help students understand the technical factors, human

factors, and economic factors involved in GI engineering in the

real world, and have a more real sense of abstract concepts, such

as safety, risk, cost, and eﬃciency.

(3) The ethical situational education of GI engineering.

Situational education is the most important key education.

How to realize the coordination between international vision

and regional discourse in the development of GI EEE has

become a key issue to be considered urgently. To construct

the Chinese discourse of GI EEE, we need to absorb the latest,

scientiﬁc, and advanced achievements from the world and take

the lead in the development of The Times. In designing the

subject system, teaching system, teaching material system, and

management system, it is necessary to pay attention to absorbing

new educational ideas and scientiﬁc teaching methods. It is

necessary to make new progress in practical cooperation and

broaden the space and object of GI EEE. In essence, the ethical

education of GI engineering is the ideological work of human

beings. It is necessary to re-understand the ethics education

of GI engineering from the height of ideological and political

education. Colleges and universities will bring GI EEE into the

big ideological and political pattern, and further improve the

status of GI EEE. In the course design, course teaching, and other

links, professional course teachers dig deeply into the ethical

norms, value orientation, and engineering spirit contained

in professional courses. In addition, GI engineering ethics

teachers can show the mission of Chinese engineers with the

help of fresh materials of “technological anti-epidemic” in the

course design.

Conclusions

Based on the key mechanism of GI EEE proposed above,

the initial evaluation index system of the implementation

status of the key mechanism of GI EEE is constructed.

Firstly, the fuzzy analytic hierarchy process is used to assign

weights to each index based on 35 expert weights. It can

be found that the importance of diﬀerent evaluation levels

and indicators for EEE is diﬀerent, and all evaluation levels

and indicators maintain a dynamic balance. The importance

of the cultivation mechanism was the highest, while the

importance of the collaborative mechanism and the situational

mechanism decreased in descending order. Secondly, based on

219 evaluation samples, the fuzzy comprehensive evaluation

method is used to empirically evaluate the implementation

status of key mechanisms of GI EEE. Finally, on the basis of the

Frontiers in Psychology 15 frontiersin.org

Yin and Zhang 10.3389/fpsyg.2022.926133

evaluation, the fact-oriented objective judgment is made on the

key mechanism of GI EEE.

The results of this study are as follows. (1) The key

education of GI EEE includes cultivation education of micro

dimension, collaborative education of medium dimension,

and situational education of macro dimension. The micro

dimension of training education is to enhance the quality of

the supply of higher education. Meso-dimension collaborative

education is the collaborative power of ethical education.

Macro-dimension situational education is the ﬁeld space of

developing regional context. (2) The factor aﬀecting the ethical

education of GI engineering is cultivation education. At present,

the most important education is to strengthen the education

of GI engineering ethics in the training process of college

students. The coordination of GI EEE is becoming more and

more important, and the integration and construction are the

important pursuit of GI EEE. (3) The ethical education of GI

engineering is at a general level in the development of cultivation

education, collaborative education, and situational education.

The implementation status of the key education of GI EEE is a

good deviation, and there is still a large space for improvement.

The key direction of promoting the internationalization of GI

EEE from an international perspective is to actively carry out

educational dialogues, including grasping the global frontier

situation and theory localization. The government’s willingness

to participate in the ethical education of GI engineering is

not enough, especially the ethical literacy of talents has not

been widely valued by the government. The optimized space

of training education includes teaching cases and full-cycle

ethical education. (4) Realize the dynamic balance of training

and education on the connection between supply and demand.

Enrich the governance means of GI EEE. Deepen the ethical

situational education of GI engineering.

Although the purpose of this study has been achieved, there

are still some issues that need to be studied in the future. One is

the lack of dynamic changes in evaluation data. Future studies

can consider the dynamic changes of key variables in time series

so as to accurately grasp the evolution trend. The second is

about the sample structure. In the survey samples, teachers and

scholars are in the majority. The small sample size of engineering

community managers may aﬀect the conceptual validity of the

survey results to some extent. Future studies should increase the

sample size of engineering community managers.

Data availability statement

The data presented in this study are available on request

from the corresponding author.

Author contributions

SY and NZ: conceptualization and validation. SY:

methodology, software, and writing—review and editing.

NZ: writing—original draft preparation. Both authors have read

and agreed to the published version of the manuscript.

Funding

This research was funded by the 11th Batch of Teaching

Research Projects of Hebei Agricultural University in 2021,

Research on Interdisciplinary Cultivation and Reading Practice

Teaching Mode of Agricultural College Students (2021C-

39), the Construction of Ideological and Political Case Base

of Organizational Behavior (2021B-2-01), the Project of the

Chinese Association of Degree and Graduate Education,

Research on the Cultivation of Practical Ability of Graduate

Students Majoring in Agronomy (2020MSB37), and Hebei

Agricultural University First-Class Undergraduate Course

Construction Project Management System Engineering.

Conﬂict of interest

The authors declare that the research was conducted in the

absence of any commercial or ﬁnancial relationships that could

be construed as a potential conﬂict of interest.

Publisher’s note

All claims expressed in this article are solely those of the

authors and do not necessarily represent those of their aﬃliated

organizations, or those of the publisher, the editors and the

reviewers. Any product that may be evaluated in this article, or

claim that may be made by its manufacturer, is not guaranteed

or endorsed by the publisher.

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Dec 2022

With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.

Enhancing Digital Innovation for the Sustainable Transformation of Manufacturing Industry: A Pressure-State-Response System Framework to Perceptions of Digital Green Innovation and Its Performance for Green and Intelligent Manufacturing

Article

Full-text available

May 2022

Low carbon and digitalization are the general trends of manufacturing upgrading and transformation. Digital technology enables the whole process of green manufacturing and breaks down the spatial barrier. To achieve the dual carbon goals, the pressure-state-response (PSR) model, in which digital technology enables the green innovation of the manufacturing industry, was theoretically analyzed in this study. The measurement system of the digital green innovation (DGI) in the manufacturing industry was constructed according to the PSR framework. An evaluation model based on the analytic hierarchy process and the deviation maximization technique for order preference by similarity to an ideal solution method was constructed to measure the level of DGI. The results of this study from Chinese manufacturing are as follows. (i) The measurement system of the level of DGI in manufacturing industry includes a pressure system, state system and response system. (ii) In the past five years, the comprehensive index of the DGI in manufacturing industry has generally shown a trend of fluctuating rise. There are overall low and unbalanced phenomena in all regions. The gap decreased from 0.1320 to 0.1187, showing a gradually narrowing trend. (iii) Compared with other regions, the composite index of DGI is generally higher in the regions with a better ecological environment in the east and a more developed economy in the north. State parameters are higher than pressure and response parameters in most areas. (iv) Compared with other regions, the composite index of DGI in western and southern regions is lower, and the parameters of pressure, status and response are basically coordinated. (v) The application degree of digital technology, the emission intensity of waste water/exhaust gas of output value of one hundred million yuan and the expenditure intensity of digital technology adopted by enterprises are the key influencing factors of DGI in the manufacturing industry. This study not only proposed an evaluation index system of the digital green innovation level, but also puts forward policy guidance and practical guidance of digital technology to accelerate the green and intelligent manufacturing industry.

Developing a Conceptual Partner Selection Framework: Digital Green Innovation Management of Prefabricated Construction Enterprises for Sustainable Urban Development

Article

Full-text available

May 2022

Digital green innovation management activities are the core of low-carbon intelligent development of prefabricated construction enterprises (PCEs) for sustainable urban development. PCEs have to seek joint venture partners to avoid the financial risk of digital green innovation projects. The purpose of this study is to develop a conceptual partner selection framework for the digital green innovation management of prefabricated construction towards urban building 5.0. In this study, first, symbiosis theory and six analysis methods were integrated to innovatively build a 3W1H-P framework system for the joint venture capital partner selection of digital green innovation projects. Second, the dual combination weighting method was innovatively proposed to avoid subjective and objective deviation in attribute weight and time weight. Finally, empirical research was carried out to verify the scientific nature, reliability, and practicability of the framework system and selection model. The results of this study show that the framework system and selection model proposed can be used to assist PCEs to select joint investment partners of digital green and innovative projects for sustainable urban development.

Conceptual Tools to Inform Course Design and Teaching for Ethical Engineering Engagement for Diverse Student Populations

Article

Full-text available

Apr 2022
SCI ENG ETHICS

Contemporary engineering education recognises the need for engineering ethics content in undergraduate programmes to extend beyond concepts that form the basis of professional codes to consider relationality and context of engineering practice. Yet there is debate on how this might be done, and we argue that the design and pedagogy for engineering ethics has to consider what and to whom ethics is taught in a particular context. Our interest is in the possibilities and challenges of pursuing the dual imperatives of socialization and critique. Socialization involves creating opportunities for all, in a diverse cohort of students, to access and engage with the dominant professional engineering ethics knowledge, while critique involves engaging with a range of ways of knowing, valuing, being and using language as relevant in contemporary engineering practice. We identify conceptual tools from engineering ethics and ethical pedagogy in education scholarship for our context. We illustrate how we use these tools systematically to strengthen our reflective practice in a first-year university engineering ethics module to a deeper form of reflexivity. Specifically, we explore the ways in which we attend to the dual imperatives and also highlight opportunities that we miss. We identify as key opportunities design choices such as how we formulated questions and prompts, and how we attended to content, context and language in selecting classroom texts. Other key opportunities were pedagogical choices of when and how to use student contributions in discussion, and what was made explicit in the classroom and assessment. We share our plans to take our learnings forward in our practice and consider the generative possibilities of these learnings and the concepts in other contexts.

Evaluating the effectiveness of bioethics education through quality standards and indicators (the link to access the article: https://rdcu.be/cAopp)

Article

Full-text available

Apr 2022

Ercan Avci

Bioethics education has remarkably grown across the globe in the last few decades. The data of the UNESCO Global Ethics Observatory proves that bioethics education is a global phenomenon with various bioethics institutions, teaching programs, and academicians. However, this situation does not mean that bioethics education has reached a stable and flawless level. Especially, the effectiveness of bioethics programs or their quality assessment is a major area that should be analyzed through more academic works. For filling that gap, this article aims to formulate a general framework to address the quality standards and indicators in bioethics education. The article adapts Donabedian’s three approaches—structure, process, and outcome— as three levels to measure quality in bioethics education. The proposed model considers the indicators the ultimate quantifiable points by creating a sequential classification as the three levels, main categories, standards, and indicators, which means that it applies an inductive approach following a path from the indicators to the standards, from the standards to the main categories, and from the main categories to the levels in order to reach a conclusion about the effectiveness of each level.

A Multi-level Review of Engineering Ethics Education: Towards a Socio-technical Orientation of Engineering Education for Ethics

Article

Full-text available

Aug 2021
SCI ENG ETHICS

This paper aims to review the empirical and theoretical research on engineering ethics education, by focusing on the challenges reported in the literature. The analysis is conducted at four levels of the engineering education system. First, the individual level is dedicated to findings about teaching practices reported by instructors. Second, the institutional level brings together findings about the implementation and presence of ethics within engineering programmes. Third, the level of policy situates findings about engineering ethics education in the context of accreditation. Finally, there is the level of the culture of engineering education. The multi-level analysis allows us to address some of the limitations of higher education research which tends to focus on individual actors such as instructors or remains focused on the levels of policy and practice without examining the deeper levels of paradigm and purpose guiding them. Our approach links some of the challenges of engineering ethics education with wider debates about its guiding paradigms. The main contribution of the paper is to situate the analysis of the theoretical and empirical findings reported in the literature on engineering ethics education in the context of broader discussions about the purpose of engineering education and the aims of reform programmes. We conclude by putting forward a series of recommendations for a socio-technical oriented reform of engineering education for ethics.

Teaching engineering ethics: a dissenting voice

Article

Full-text available

Jun 2021

Rob Lawlor

The reference to ‘a dissenting voice’ in the title has a double meaning. On the one hand, this paper itself provides a dissenting voice, in that it challenges a number of common practices and widely held views. (For example, challenging the typical focus on case studies, and the focus on the decision-making of individual engineers.) In addition though, the paper argues that teachers of engineering ethics should be willing to discuss the state of the profession, and be willing to criticise the profession and the professional institutions (where appropriate), providing a dissenting voice and aiming to inspire the engineers of the future to challenge the status quo and ultimately to strengthen the profession.

Let’s Play! Gamifying Engineering Ethics Education Through the Development of Competitive and Collaborative Activities

Conference Paper

Jul 2021

The obstacles to preparing ethical teachers for Arab schools in Israel: The case of one Arab college of education

Article

Apr 2022

Najwan Saada

This multiple case study explores the obstacles to preparing prospective Arab teachers to deal with values/moral issues, from the perspective of Arab teacher educators in one Arab college of teacher education in Israel. In-depth interviews with 13 teacher educators were conducted and analysed. Four obstacles were reported by teacher educators. The first was the scholastic-oriented curriculum in their college and the exclusive focus on developing the student teacher’s technical skills, disciplinary knowledge and intellectual capacities. The second was related to the teacher educators’ qualifications—their motivation and professional awareness with respect to dealing with values/moral issues in their courses. The third was related to the quality of the student teachers, their desire to become teachers, their commitment and their understanding of the moral work of teaching. The fourth pertained to the college of education’s culture and the lack of a systemic policy of preparation for values education.

Real-Time Sensor Networks, Advanced Robotics, and Product Decision-Making Information Systems in Data-driven Sustainable Smart Manufacturing

Article

Dec 2020

We draw on a substantial body of theoretical and empirical research on data-driven sustainable smart manufacturing, and to explore this, we inspected, used, and replicated survey data from BDV, Capgemini, Deloitte, MHI, PwC, Techconsult, we.CONECT, and World Economic Forum, performing analyses and making estimates regarding the link between real-time sensor networks, advanced robotics, and product decision-making information systems. Structural equation modeling was used to analyze the data and test the proposed conceptual model.

Information fusion for future COVID-19 prevention: continuous mechanism of big data intelligent innovation for the emergency management of a public epidemic outbreak

Article

Jul 2021

Information fusion is very effective and necessary to respond to a public epidemic outbreak such as COVID-19. Big data intelligent, as a product of information fusion, plays an important role in the prevention and control of COVID-19. The continuous mechanism of big data intelligent innovation (BDII) is fundamental to effectively prevent and control a public epidemic outbreak. In this study, the continuous mechanism of BDII was fused into a complex network, and a three-dimensional collaborative epidemic prevention model was constructed. Furthermore, adiabatic elimination principle was applied to explore the order parameter of the continuous mechanism. Finally, empirical analysis was conducted based on three-stage epidemic prevention strategies to reveal the effect of continuous epidemic prevention under different big data intelligent emergency management policy levels. The results of this study are as follows. Through the mutual influence and coupling of the subsystems, the continuous mechanism of BDII can be realized to manage a public epidemic outbreak emergency. The big data intelligent subsystem is integrated into the subsystems of public epidemic outbreak management and science and technology innovation. The big data intelligent emergency management policies play a positive role in the overall BDII for the continuous epidemic prevention of a public epidemic outbreak. The convention of BDII transformation is the continuous mechanism of BDII as the order parameter of a public epidemic outbreak. In the early stage of epidemic prevention, the convention is excessively pursued, while the neglect of BDII configuration is not conducive to the long-term collaborative governance of a public epidemic outbreak. The study provides practical guidelines for the formulation of fusion innovation policies, application of big data intelligent, and theoretical basis for the emergency management of a public epidemic outbreak in the medical field.

Enhancing engineering ethics education (EEE) for green intelligent manufacturing: Implementation performance evaluation of core mechanism of green intelligence EEE

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