Student Readiness Model Facing the Industrial Revolution 4.0

Dedek Andrian and Astri Wahyuni

Department of Mathematics Education, Universitas Islam Riau, Pekanbaru, Indonesia

Keywords:

Determinants, Student Readiness, Industrial Revolution

Abstract:

The purpose of this study is to describe the factors that inﬂuence the student readiness in facing the industrial

revolution 4.0. This study uses quantitative research methods. The population in this study was the student

teacher of education faculty of Universitas Islam Riau. The sampling technique used is cluster random

sampling. The data collection technique uses a questionnaire. Data analysis technique uses Product Moment

Correlation and Path Analysis with the help of Lisrel 8.80 Software. The results showed that; (1) there is no

signiﬁcant effect the variable of Parent Support (PS) and Learning Environment (LE) on Industrial Revolution,

(2) there is signiﬁcant effect learning facilties (LF) and university policy (UP) on Industrial Revolution (IR),

(3) there is signiﬁcant effect of university policy and parent support (PS) on learning facilities, (4) there is

signiﬁcant effect of learning facilities (LF), parent support (PS) and university policy (UP) on the learning

environment (LE).

1 INTRODUCTION

The beginning of the industrial revolution allowed

all preparations to be made by every university.

This is doing so that every university is able to

keep up with the ﬂow of globalization that is

difﬁcult to be stopped. The world is entering the

era of 4.0 industrial revolution which is followed

by technology developments and emphasizes on all

digital (digital economy, artiﬁcial intelligence, big

data, robotic), or the phenomenon of disruptive

innovation. Universities need to pay attention to

the factors that inﬂuence the readiness of students in

facing the industrial revolution because the success

of students facing the 4.0 industrial revolution

depends on what is prepared both from students and

universities.

Global competitiveness index data shows that

Indonesia’s ability to face the 4.0 industrial revolution

is still lower compared Thailand which is at level

32, Malaysia ranks 23 and Singapore is at level

3. The factors that cause Indonesia still cannot

compete with Malaysia, Thailand, and Singapore

are weak in higher education, lack of training or

workshops, the knowledge readiness of science and

technology, innovation and satisfaction in doing

business (Kemenristek Dikti, 2019).

These aspects need to be improved so that

Indonesia’s ability to face the 4.0 industrial revolution

is better and on top of Singapore.

Resources strengthening is very important for

universities, resources strengthening includes the

development of lecturer abilities, learning facilities,

learning environment, campus policy, and parental

support. Resources that are the determinants factors

of success in facing the industrial revolution need

to be strengthened so that the ability of students

to face the ﬂow of globalization in the era of 4.0

Industrial Revolution is not doubted because students

are supported by strong and good facilities and

resources.

Responding to the importance of competition in

facing 4.0 industrial revolution, higher education

speciﬁcally universities need to provide good quality

services. In addition, the research needs to be

done for knowing whatever the thing must be

increased, repaired, and prepared so that research

can be used to determine what factors are capable

of making students ready to face the 4.0 industrial

revolution. Based on this reason, the research

titled ”The Inﬂuence of Parent Support, Learning

Facilities, Learning Environment, and Universities

Policy, on Student Readiness in Facing the 4.0

Industrial Revolution is very important to be done.

302

Andrian, D. and Wahyuni, A.

Student Readiness Model Facing the Industrial Revolution 4.0.

DOI: 10.5220/0009128703020306

In Proceedings of the Second International Conference on Social, Economy, Education and Humanity (ICoSEEH 2019) - Sustainable Development in Developing Country for Facing Industrial

Revolution 4.0, pages 302-306

ISBN: 978-989-758-464-0

2 LITERATURE REVIEW

Learning facilities are an important factor in getting

maximum learning outcomes. Quality of learning

facilities is a determining factor in improving student

performance (Uline and Tschannen-Moran, 2008).

Learning facilities such as online-based learning can

improve student satisfaction in learning and inﬂuence

learning outcomes or student achievement (Eom

et al., 2006). Learning facilities create value or

learning experiences that can produce better learning

outcomes (Parnabas et al., 2014). Learning facilities

can improve the quality of learning where students

get learning experiences that are different from the

previous process (Bliuc et al., 2007). Learning

facilities have a signiﬁcant relationship to student

achievement, so it can be concluded that the better

learning facilities, the better the learning outcomes

(Keramati et al., 2011).

The learning environment is a factor that can

inﬂuence the success of the learning process. The

learning environment has an important role in

increasing interest and learning outcomes (K

arn

and Julin, 2015). A conducive environment for

learning can improve learning outcomes (Kleij &

Fesken, 2015). A different or less conducive

environment can be signiﬁcantly different from the

results of a conducive learning environment (Khalifa

and Lam, 2002). The learning environment can make

learning activities interesting because the learning

environment becomes a stimulus for students to

achieve success in learning (Rasch and Schnotz,

2009). The learning environment is a determining

factor for the success of a teaching and learning

process.

The success of students is also inﬂuenced by

the stake holder’s policy. Stakeholders who tend to

have the desire to advance education through policies

that build student achievement (Price and Roberts,

2008). Governments that have the same policies as

leaders in each educational institution can produce

good learning outcomes (Ngware, Onsomu, Muthaka,

& Manda, 2006). Collaboration between government

and educational institutions can realize educational

goals in producing great educational output (Bektas¸

and Tayauova, 2014). Government policies that

speciﬁcally have their own ﬁelds of education can

affect the educational institutions below them through

programs aimed at improving education (Lumby and

Wilson, 2003).

Parent support is very important in student success

of learning both at school as well as the university.

Parent support is described as the main support

because they have an enormous effect in developing

the student to achieve success in its life (Creech,

2010). Parental involvement will help students more

if they are supported by easily accessible technology

(Lewin and Luckin, 2010).

3 RESEARCH METHODOLOGY

The research method used in the study is a

quantitative research method with a correlational

approach. This research aims to ﬁnd the relationship

or inﬂuence of one or several independent variables

on the dependent variable. Creswell (2014) states

that the correlational research method can in the

form of correlation, regression, path analysis, and

Structural Equation Modeling (Creswell, 2012; Hair

et al., 2013).

3.1 Population and Sample

The population in this study were all Teachers

and Education Faculty students, while the sample

was some of the Teaching and Education Faculty

students who were taken randomly. The sampling

technique used in this study is the stratiﬁed random

sampling technique, the number of research samples

was 265 students. In the context of this study,

researchers will randomize students in all Teaching

and Education Faculty Study Programs based on the

level or semester taken by Students, that is 2, 4, 6, 8.

3.2 Research Instrument

The data collection instrument used in this study

was a questionnaire. A questionnaire was developed

based on studies of variables so that indicators were

found. Based on these indicators, items are arranged

to obtain data in the ﬁeld.

3.3 Validity and Reliability of

Instrument

The validity of the instrument in this study used

content and construct validity while reliability

used construct reliability and Cronbach Alpha.

Content validity was analyzed using Aiken’s Formula

while construct validity was analyzed using CFA

(Conﬁrmatory Factor Analysis). Construct reliability

was analyzed using CFA based on the value of loading

factor and error, while Cronbach Alpha reliability

used the formula from Cronbach Alpha.

Student Readiness Model Facing the Industrial Revolution 4.0

303

3.4 Analysis Data Technique

Data analysis in this study using product moment

correlation and path analysis. Product Moment

correlation is used to see the relationship between

independent variables to other independent variables

and independent variables to the dependent variable

with the help of SPSS 16. Path analysis is used

to see how far the independent variable affects the

dependent variable and see how far the independent

variables affect the other independent variables as

moderator. Path analysis was carried out with the help

of the Lisrel 8.80 program.

4 RESULT

4.1 Product Moment Correlation

Product moment correlation in this research aims

to see the correlation between variables of parent

support, learning facilities, university policy, learning

environment, and readiness of student facing the

industry revolution 4.0. the result of product moment

correlation can be seen at the correlation matrix in

Table.

Table 1: Correlation Matrix

PS LF UP LE IR

PS 1 .553** .515** .583** .319**

LF .553** 1 .717** .652** .563**

UP .515** .717** 1 .657** .582**

LE .583** .652** .657** 1 .477**

IR .319** .563** .582** .477** 1

**= signiﬁcant with level 95%

Based on Table 1 were acquired the result of

analysis of product moment correlation from all

variables. From analysis has found that there is a

signiﬁcant correlation between independent variables

PS with LF, UP, LE and IR with values consecutively

are o.553, 515, 583, 0.319. there is a signiﬁcant

correlation between LF variable with UP, LE, and IR

with values consecutively are 0.717, 0.622, and 0.563.

There is a signiﬁcant correlation between UP variable

with LE and IR with values consecutively are 0.657

and 0.582. There is a signiﬁcant between LE variable

with IR variable. These results show that all variables

have the good correlation and signiﬁcant with level

95%.

4.2 Path Analysis Result

Path analysis will describe the effect of the

independent variable on independent variables and

independent variables (moderation variables) on

dependent variables. in this research, parent support

(PS) as independent variables. Learning facilities

(LF), university policy (UP), learning environment

as moderation variables, and student readiness facing

the industrial revolution as the dependent variable.

Before the result of the analysis is shown. the ﬁrst

step, the researchers must know that the analysis

result has got good data which is the measurement

model has developed. the data was said well if the

Goodness of Fit that acquired from the analysis result

has fulﬁlled criteria. The result of the analysis can be

seen in Table 2.

Table 2: Standard and Result of Goodness of Fit.

GOF Standard Result Conclusion

Chi-Square P >0,05 0,4421 Fit

RMSEA RMSEA <0.08 0,000 Fit

GFI GFI ≥ 0.90 0.96 Fit

NFI NFI ≥ 0.90 0.95 Fit

CFI CFI ≥ 0.90 0.96 Fit

IFI IFI >0.80 0.96 Fit

RFI RFI 0-1 0.91 Fit

From table 2 was got seven criteria of GOF. this

result show that the data were collected at the ﬁeld are

ﬁt and fulﬁlled the criteria the good data. This result

indicates that the measurement model has developed

from studying the theory in accordance that was

proposed by the statistics expert. because of the result

has fulﬁlled the good criteria, so the result of path

analysis can be continued or interpreted. The result

of path analysis can be seen in Table 3.

Table 3: The Result of Path Analysis.

Variables Coefﬁcient T-Value Criteria

PS→LF 0,25 3,91 Signiﬁcant

PS→UP 0,52 7.26 Signiﬁcant

PS→IR -0,08 -0.01 Not Signiﬁcant

PS→LE 0.27 3.88 Signiﬁcant

UP→LF 0,59 9.17 Signiﬁcant

UP→LE 0,32 3.98 Signiﬁcant

UP→IR 0,35 3.46 Signiﬁcant

LF→LE 0,27 3.23 Signiﬁcant

LF→IR 0,29 2.88 Signiﬁcant

LE→IR 0,11 1.14 Not Signiﬁcant

From Table 3 above can be concluded that there

is signiﬁcant effect variable PS on LF, UP, and LE

with the contribution of each variable are 25%, 52%,

and 27% but there is no signiﬁcant contribution PS

variable on IR variable. There is a signiﬁcant effect

of UP variable on LF, LE, and IR with contribution

are 59%, 32%, and 35%. There is a signiﬁcant effect

of LF variable on LE and IR with contribution are

27% and 29%. There is no signiﬁcant effect of LE

ICoSEEH 2019 - The Second International Conference on Social, Economy, Education, and Humanity

304

variable on IR variable. The biggest contribution in

this research is UP variable on LF variable and the

smallest contribution is LE variable on IR variable.

5 DISCUSSION

From the result of analysis has got the effect of

independent variables on the dependent variable. the

analysis shows there is an effect of Parent support

on Learning Facilities. This information explains

that parent support gives a good contribution to

learning facilities. Dempsey (2001) said that parental

involvement in education is very important as a

school-improvement effort. Parental involvement in

educational setting and activities are the important

thing for the schools because the parent can give

any contribution to the school for creating the good

school that educates the student from various useless

things (Hara and Burke, 1998). Parent support has

a good effect on learning Environment because the

student parent is an element of school that gives

recommendation about the learning environment and

will care with learning environment (Hara & Burke,

2019). Parent support in form learning facilities will

give an opportunity to the student for developing their

skill (Hoefer et al., 2001). Family support will have a

tremendous role in helping the student adapt to rapid

technology development (Ratelle et al., 2005).

There is a signiﬁcant result of parent support

on university policy, learning facilities, and learning

environment. this result explains that university

policy depends on parent support to make policy

for creating good facilities and environment. the

college collaboration with the parent of student will

make education become more effective (Ratelle et al.,

2005). Parent and school need to work together

for achieving the maximum outcome. Schools have

accountabilities about the academic outcome, and the

parent has accountabilities about moral, cultural, and

religious (Hill and Taylor, 2004).

From analysis was acquired negative contribution

of Parent support on the Student readiness facing the

industry revolution 4.0. this thing happens because

many of parent doesn’t have knowledge about the

important facing the industry revolution 4.0 so they

don’t think how to motivate their children to face the

industry revolution 4.0. Anderson & Minke (2010)

said that parent involvement in student study can give

a positive outcome for the student but it depends on

how the parent decides for motivating their children.

Effect of learning facilities on the learning

environment and the readiness student facing the

industry revolution 4.0 is signiﬁcant. this result

shows that facilities are an important factor in

making the conducive environment of learning

and good outcome. student performance in the

classroom depends on learning facilities and facilities

management (Leung and Fung, 2005). Bad facilities

of learning can make the learning environment

uncomfortable and give a negative impact on student

outcome (Earthman, 2002). Learning facilities is very

important for increasing the quality of education.

6 CONCLUSIONS

Based on the analysis result can be made the

conclusion that there are two factors or variables

were affected the student readiness in facing the

industry revolution 4.0 that is Learning Facilities with

contribution 25% and University Policy (52%) while

Learning Environment (27%) and Parent Support

can’t contribution on the student readiness in facing

the industry revolution 4.0. Biggest contribution on

the student facing the industry revolution variable

is University Policy variable while the smallest

contribution on the student readiness in facing the

industry revolution 4.0 is Learning Facilities.

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