Research On Conditions For Implementation Of Smart School Model In Hanoi

RESEARCH ON CONDITIONS FOR IMPLEMENTATION OF SMART SCHOOL MODEL IN HANOI Nguyen Trung Hien (VNU International School) Vu Minh Trang, Lai Phuong Lien (Faculty of Pedagogy, VNU University of Education) Bui Thi Thuy Hang (School of Engineering Pedagogy, Hanoi University of Science and Technology) Abstract The rapid advances of the industrial revolution 4 0, along with the trend of globalization, international integration, and outstanding achievements of the knowledge economy, requires Vietnamese ed[.]

OF SMART SCHOOL MODEL IN HANOI

Nguyen Trung Hien

(VNU International School)

Vu Minh Trang, Lai Phuong Lien

(Faculty of Pedagogy, VNU University of Education)

Bui Thi Thuy Hang

(School of Engineering Pedagogy, Hanoi University of Science and Technology)

Abstract: The rapid advances of the industrial revolution 4.0, along with the trend of globalization,

international integration, and outstanding achievements of the knowledge economy, requires Vietnamese education to adapt and make efforts to innovate its policies, contents and training methods for a generation of citizens to meet the requirements of human resources in modern society Therefore, the purpose of the study is to build a theoretical basis for smart schools, to design 12 criteria for evaluating smart school models to assess the status quo and the accessibility to the smart school model at all educational levels in Hanoi, Vietnam The study was conducted for 238 primary school pupils, 244 secondary school pupils and 245 high school students in 25 schools in the inner city and suburbs of Hanoi The survey questions cover six aspects: perceptions of learning, perceptions of teachers, perceptions of the schools, methods of communication with teachers, learning materials, facilities, and smart services The results received through the data analysis have shown a panorama picture of the current situation in schools, thereby, assessing the ability to apply the smart school model in Hanoi based

on the evaluation criteria of the smart school model in the world and within the country Then, the next step is

to propose recommendations for the development and implementation of a suitable smart school model to the practical conditions of schools in Hanoi.

Keywords: smart school model, schools in Hanoi, the evaluation criteria of the smart school.

1 INTRODUCTION

To build a smart nation, many countries have aimed to build a smart education to train generations

of smart citizens As an advanced school model, smart schools create opportunities and conditions for schools to enhance their adaptive capacity and balanced development in the need of rapid changes

in society in general; Learners can discover and create knowledge, develop self-control and adaptive capacity, and think creatively through personalized pedagogical instruction, tailored to their individual characteristics and needs (Ghonoodia & Salimi, 2011); increase the importance, reliability, usefulness, flexibility of the curriculum content The application of smart technology to school education has reshaped the educational landscape by transforming the contents and methods of receiving/delivering learning as well as the ways of guiding, supporting, organizing, and managing school (Attarana et al., 2012)

With the smart school model, learners are considered to be the center of the educational process, in which learners can express their perceptions of the world, develop personal capacities, and express their ideas independently in virtual classrooms so that good learners quickly access more complex contents,

Therefore, the model also creates an equal learning environment, suitability, fairness, and objectivity in the educational process; and narrows the gender gap, between classes in society, towards narrowing the gap between countries

The advancement of science and technology help teachers to perform their professions well, effectively delivering knowledge, easily creating learning activities to develop soft skills, life skills for students, and creating the best learning conditions for their students In smart school model, teachers and students will interact with online lectures, and lessons via virtual reality to improve capacity, skills and save time At the same time, students can approach high technology and express their abilities and with teachers to create lesson content with practical applications Compared with the traditional school model, the smart school model focuses on innovation and optimization of equipment and teaching resources, opening opportunities for two-way interaction between students and teachers, enabling students to be more active during class time It is a good and favorable condition for students to develop and apply creatively the results obtained in the learning process (Dinh Van De, 2018)

For the above reasons, Vietnamese education needs to deploy the smart-school model as soon as possible It is recommended to try first in Hanoi, to set up an educational institution that flexibly and effectively uses resources based on applying digital technology advancement to improve the quality of education, meeting the requirements of society in training the young generation (Vu Thi Thuy Hang, 2018)

2 CRITERIA FOR EVALUATING SMART SCHOOLS IN THE WORLD AND VIETNAM

Bătăgan and Boja (2011) mentioned that when implementing a smart education system, it is necessary to pay attention to the following factors: 1) A system for data collection, synthesis, analysis and presentation of information on key factors, knowledge and school orientation; 2) Clustered education,

to involve all stakeholders in the educational preparation for future generations; 3) Cloud Computing

at school, whereby learners can connect, access different software and hosted resources The use of technology is expected to make it easier for all ages and levels to access educational features from public libraries to computer classes, providing training and higher education to support the implementation

of formal education in the real world With the spread of IT, cities can revolutionize the relationship between learners and teachers as well as schools and educational activities

A study by Frost & Sullivan (2014) titled Strategic Opportunity Analysis of the Global Smart City Market, mentioned that the variables of smart education including policies and digital services from the support of the Government to implement a smart and green solution through incentives, subsidies, promotions and others Smart Education includes eLearning services for schools, universities, businesses and government organizations Smart education in 2020 will have the biggest business opportunity among various aspects of smart city, accounting for 24.6% of market demand in the smart city industry

A study by Deloitte (2015) with the title: Smart Cities - Rapid advancement in technology is reshaping our economy and society mentioned that in the field of smart education, Education that can be done in the smart city is an education that supports virtual, digital, augmented reality (AR) learning that has changed the way students learn Education, equipped with rich data and abundant analytics will help teachers tailor learning and mentoring practices for students’ success The focus of the teaching process has changed from digital content to real-world experiential learning, where most students, teachers and professionals connect – the environment most suitable for learning

A study by Supangkat et al., (2018) mentioned that technology is used in the process and IT products are used to solve problems in education and learning activities in Indonesia To develop strategies for optimal and appropriate use of learning technology, the important thing to consider is existing conditions Smart cities show massive support to the use of technology to improve government performance and citizen welfare as well as cut government spending The smart city also produces technology and encourages the community to participate and invest in education, smart city is expected

to cooperate with the education office at the local government level

The study by citiAsia Inc (2016) shows that the development of digital technology must be used

to improve the quality of learning and education, starting from the way of school enrollment to the teaching/learning process, student assessment and technology use in the form of applications and systems for management By developing smart school applications, the school management can become more efficient, starting from checking the attendance of students and teachers to the financial organization of the school Apps and systems have been used as a tool for more efficient academic assessment, payment and campus management, as well as making the educational process available to more people The concept of smart education in a smart city must ensure educational opportunities for groups of people who have not had the opportunity to learn The existence of digital technology as part of smart education also allows any local government to improve the community’s access to acquiring knowledge through digital libraries or to facilitate management and exchange knowledge for the community

Halim et al., (2005) proposed four parameters that contribute to the success of the smart school system These four parameters are the teacher’s curriculum, pedagogic method, assessment, and learning materials The Smart Curriculum will ensure that children are equipped with critical and creative thinking skills, inculcate appropriate values and are encouraged to improve their language proficiency The curriculum will therefore be designed to help learners achieve overall balanced development; internalize knowledge, skills, values and correct use of language; state the intended learning outcomes for different ability levels; provide multidisciplinary, thematic and continuous learning; cultivate the right knowledge, skills and attitudes for success in the Information Age (Jaafar, 2008) In terms of pedagogic methods, smart pedagogical schools will seek to make learning more enjoyable, motivating, stimulating and meaningful; intrigue students’ minds, spirits and engage physical bodies in the learning process; build basic skills to help students prepare for greater challenges in the long run and serve a wide range of needs and abilities of learners Smart school assessments will be flexible and learner-friendly while ensuring the quality of assessment information using a variety of methods and tools Smart schools will also need teaching materials designed for new teaching strategies These materials will meet the diverse needs and abilities of learners, leading to a comprehensive realization of their potential, and allowing learners to take greater responsibility in managing and monitoring their learning

In addition, the learning environment is also another important factor to consider in the learning process The learning environment must be supportive and encouraging and provides learning opportunities for both students and teachers

According to Zajda and Donna (2009), the operation of Smart School is different from that of a regular school Authority in school management is assigned to both staff and stakeholders, so relevant data can be collected and used to ensure the school fits into the local teaching and learning environment School governance also involves pedagogical communication, and review of policy, curriculum, community, and school management Learner’s issues include expertise, assessment, implementation, consultation, health, testing, management, medical, insurance, v.v

We can summarize the study above into 12 criteria evaluating a smart school that the authors mentioned as shown in Table 1 below:

Table 1 Criteria for smart schools through analyzed studies Criteria (2005) Halim Bătăgan (2011) Sullivan Frost &

(2014)

Deloitte (2015) Supangkat (2015) Inc (2016) CitiAsia

Online system, network connection, data

In Vietnam, the smart school model is still quite new, but from 2003-2004 up to now, many provinces and cities have developed projects or are piloting this model, such as: Hanoi, Ho Chi Minh City, Da Nang, Quang Ninh, Thanh Hoa, Thai Binh, Hai Phong In the process of implementation, the schools have encountered difficulties and challenges but also achieved remarkable results However, the evaluation of the effectiveness of the current smart school model has not been conducted and implemented synchronously Therefore, the research on the evaluation/assessment criteria and assessment procedure

of the smart school model has not been widely researched and developed to prove the effectiveness of this model

According to Vu Thi Thuy Hang (2018), although there are differences, the descriptions of smart schools emphasized on several contents: (1) the goal of smart schools is to prepare and promote the workforce - the owner of the 21st century having the knowledge and skills to meet the needs and challenges of modern technological society; (2) learners is the center, provided with modern and quality learning services; (3) learning tailored to individual needs and pace, individual characteristics and circumstances; (4) the smart nature of the school that leans towards flexibility, adaptability, modernity and continuous development dynamically balances with the development of the modern technological world; (5) smart school model provides a smart educational environment for learners; (6) mart technology plays an important role in building and maintaining that smart educational environment Technology includes hardware and software Hardware is essentially devices that help learners learn effectively and conveniently, software refers to flexibility and adaptability to adaptive learning technologies such as cloud computing, big data, analytical learning, adaptive tools, create appeal for, expand development opportunities and provide services of the schools

From the studies mentioned, we can gather that smart school does not only focus on facilities and technological equipment but the smart school model also needs to be concerned with the criteria of goals and programs, assessment, teaching methods, teaching-learning materials Aside from that, there are additional factors of policy, people, process, and technology background Therefore, to evaluate the smart school model, these criteria need to be considered so that the smart school model can be evaluated most effectively

3 RESEARCH METHODS AND RESULTS

Instrument

Based on a set of criteria to evaluate the smart school model in the world and Vietnam, a questionnaire was developed including 7 areas to assess the current situation and accessibility to the smart school model in Hanoi That is:

i) Perceptions of learning, including 8 statements related to students’ feelings about learning in

general, for example: I feel interested in learning

ii) Perceptions of teachers, including 3 statements related to teaching style, the supporting of

teachers, trust and sharing with teachers For example: My teacher has an attractive teaching style

iii) Perceptions of the school, including 11 items to assess students’ feelings about learning and

living at school, for example: I feel comfortable at school

Each statement in all three areas – perceptions of learning, teachers and the school is designed on

a 4-point Likert scale ranging from Disagree (1) to Totally Agree (4)

iv) Methods of communication with teachers: explore about 6 ways that students can use to

communicate with teachers, which are: face-to-face (in class), online, forum, conference, videos, emails

v) Learning materials: Explore about 9 types of learning materials that students are provided,

which are: Textbooks, Workbooks, Worksheets, Electronic Lectures, Software/Apps, Websites, CDs, Videos, Audios

vi) Facilities: explore the facilities used for teaching in the school, including 32 items of

infrastructure/equipment/learning materials

vii) Smart services: explore about 11 smart services provided in schools

Questions about quality of smart facilities and smart services were answered on a 5-point Likert scale: None (0), Poor (1), Medium (2), Good (3), Very Good (4)

Participants

The survey subjects in this study are 238 primary school pupils (118 girls, 120 boys), aged from

8 to 11 years old, average 9.28 years old; 244 secondary school pupils (132 girls, 112 boys), aged from

11 to 15 years old, average 12.46 years old; 245 high school students (146 girls, 99 boys) from 15 to

18 years old, average 16.13 years All of whom belong to 25 schools in the city and suburbs of Hanoi

Results

Fistly, we used descriptive statistics to find out how students feel about learning, teachers and schools at all levels Then, we used Analysis of Variance to compare the mean scores of students’ perceptions of learning, of teachers, of schools at all levels Depending on the probability coefficient of

the Levene test, the Tamhane T2 or Bonferroni tests in the Post Hoc Tests will be analyzed to test the hypothesis of the difference in mean scores between groups of primary school pupils (PSP), secondary school pupils (SSP) and high school students (HSS)

3.1 Perceptions of learning

Mean score, standard deviation and range of score of students’ perceptions of learning at all levels

is shown in the table 2 below:

Table 2 Descriptive Statistics of Students’ Perceptions of Learning Variable Mean Standard Deviation Range of Score

Looking at Table 2, we can see the group of PSP has the highest average score, then SSP, and lastly, HSS has the lowest

When comparing the average score of the Perceptions of learning scale according to 3 groups

of survey subjects: PSP, SSP and HSS, the Levene test results have a probability coefficient <0.05 so there is a variance difference between the three groups Using the Tamhane T2 test in Post Hoc Tests, the results of comparing the mean scores of the 3 groups of the survey subjects and the probability coefficients are shown in Table 3

Table 3: Post Hoc Tests Statistics of Students’ Perceptions of Learning

Variable

PSP (1) (N=231)

SSP (2) (N=244)

HSS (3) (N=245) (1)-(2) (1)-(3) (2)-(3)

Perceptions of learning 24.48 22.86 22.44 0.00 0.00 0.69

Comparing the average scores of 3 groups of students, we see that there is a statistically significant difference between the groups of PSP and SSP, between PSP and HSS However, there is no statistically significant difference between the groups of SSP and HSS

3.2 Perceptions of teachers

Mean score, standard deviation and range of score of students’ perceptions of teachers at all levels

is shown in the table 4 below:

Table 4 Descriptive Statistics of Students’ Perceptions of Teachers Variable Mean Standard Deviation Range of Score

Looking at Table 4, we see, PSP have the highest average score, then SSP and the last are HSS When comparing the average score of the Perceptions of teachers according to 3 groups of survey subjects: PSP, SSP and HSS, the Levene test results have a probability coefficient >0.05 so there is no variance difference between the three groups of subjects Using the Bonferroni test in Post Hoc Tests, the statistical results comparing the average scores of the 3 groups and the probability coefficients are shown in Table 5

Table 5: Post Hoc Tests Statistics of Students’ Perceptions of Teachers Variable

PSP (1) (N=231)

SSP (1) (N=244)

HSS (1) (N=245) (1)-(2) (1)-(3) (2)-(3)

Perceptions of Teachers 9.50 8.81 8.75 0.00 0.00 1.00

Comparing the average scores of the 3 groups, we realize that there is a statistically significant difference between the PSP and SSP and between the PSP and HSS However, there is no statistically significant difference between the group of SSP and HSS

3.3 Perceptions of school

Mean score, standard deviation and range of score of students’ perceptions of school at all levels is shown in the table 6 below:

Table 6 Descriptive Statistics of Students’ Perceptions of School Variable Mean Standard Deviation Range of Score

Looking at Table 6, it can be observed that the group of PSP have the highest average score, then the group of HSS and the last is the group of SSP

When comparing the average score of the Perceptions of school according to 3 groups of survey subjects: PSP, SSP and HSS, the Levene test results have a probability coefficient > 0.05 so there is no variance difference between the three groups of subjects Using the Bonferroni test in Post Hoc Tests, the statistical results comparing the average scores of the 3 groups of survey subjects and the probability coefficients are shown in Table 7

Table 7 Post Hoc Tests Statistics Students’ Perceptions of School Variable

PSP (1) (N=227)

SSP (1) (N=244)

HSS (1) (N=245) (1)-(2) (1)-(3) (2)-(3)

Perceptions of school 33.20 30.90 32.27 0.00 0.36 0.06

Comparing the average scores of the 3 groups, we realize that there is a statistically significant difference between the PSP and SSP However, there is no statistically significant difference between the group of PSP and HSS as well as between the group of SSP and HSS

3.4 Methods of communication with teachers

Table 8 Methods of Communication with Teachers

Sum PSP

(N=244)

At all three education levels, methods of communication between students and teachers are quite diverse, in which face-to-face communication in class is the most common form, followed by online communication through social networks such as Zalo, Viber, messager, Email The methods of communication via forums, seminars, videos are less used, high school students tend to communicate with teachers more through these methods

3.5 Learning materials

Table 9 Learning Materials Provided

Mean PSP

(N=245)

Among the types of learning materials, textbooks, workbooks, and worksheets are used the most These are traditional learning materials at school Next are the types of electronic lectures, software/ apps, websites CDs, videotapes, and audio are the least used learning materials by teachers Currently, electronic lectures are a relatively common type of learning material used in teaching, we can see it is used the most at high school level but used the least at secondary school level

3.6 Facilities and technology infrastructure for teaching

Among 32 basic infrastructure/ equipment/ learning materials categories to build smart schools, students’ answers indicated that: 3 categories of Smart Classroom, STEM Classroom, Smart platforms have not yet been equipped in most primary schools; 3 categories of Student Touch Desk, E-learning lecture system, Online Question Bank are missing in many secondary schools; 3 categories of Smart Attendance Management Software System, Electronic Book System, and Online Question Bank have not been equipped in most high schools

Results of descriptive statistics of facilities quality are shown in the table 10 below:

Table 10 Descriptive Statistics of Facilities Quality Variable Mean Standard Deviation Range of Score

Looking at the table 10, we can see that the quality of facilities is rated the highest by SSP, followed

by HSS, and finally by PSP

When comparing the average score of the facilities quality according to 3 groups of survey subjects: PSP, SSP and HSS, the Levene test results have a probability coefficient > 05 so there is no variance difference among the three groups of subjects The average scores of the 3 groups of survey subjects and the probability coefficients of the Anova test in Post Hoc Tests based on the Bonferroni test are shown in Table 11

Table 11 Post Hoc Tests Statistics of Facilities Quality

PSP (1) (N=223)

SSP (2) (N=244)

HSS (3) (N=245)

(1)-(2) (1)-(3) (2)-(3)

Facilities Quality 32.82 41.08 40.80 0.00 0.00 1 00

Comparing the average scores between the two education levels, we see a statistically significant difference between the 2 groups of PSP and SSP, between the 2 groups of PSP and HSS However, there

is no statistically significant difference between the 2 groups of SSP and HSS

3.7 Smart services

Among the 11 services surveyed, the 3 most lacking services at the primary school level are: Consulting and providing appropriate digital learning materials, Smart parking service, Student transportation service; the 3 most lacking services at the secondary school level are: Smart parking service, Snack supply service, Student transportation service; the 3 most lacking services at the high school level are: Providing main meals, Snack supply service, Student transportation service

Results of descriptive statistics of smart services quality are shown in the table 12 below:

Table 12 Descriptive Statistics of Smart Services Quality Variable Mean Standard Deviation Range of Score

The results of comparing the average scores of smart service quality of the three education levels indicate that PSP have the highest scores, followed by HSS, and finally is SSP

Comparing the average score of smart service quality according to 3 groups of survey subjects: PSP, SSP and HSS, the Levene test results have a probability coefficient > 0.05 so there is no variance difference between the three groups of subjects The results of the Anova test in Post Hoc Tests based on the Bonferroni test for the average score and the probability coefficient are shown in Table 13

Table 13 Post Hoc Tests Statistics of Smart Services Quality Variable

PSP (1) (N=228)

SSP (2) (N=244)

HSS (3) (N=245) (1)-(2) (1)-(3) (2)-(3)

Smart Service Quality 14.34 13.28 14.13 0.75 1.00 1.00

The Bonferroni test results in the Post Hoc Test showed that there was no difference in the average scores between the 3 groups of subjects

4 CONCLUSION AND DISCUSSION

The overview of study results on the evaluation criteria of smart schools in the world shows that the smart school model does not only focus on facilities, technology equipment, but is also related to many other constituent factors such as learners, teachers, learning materials, learning environment Based on the approach to the constituent factors, we have built a survey sheet of implementation conditions on students at all levels from Primary, Secondary to High School The conclusions and discussions of the main findings from the survey are presented respectively below

- Perceptions of learning includes the attitudes and behaviors required in smart learners that smart schools with active teaching methods and effective exploitation of advanced technologies have contributed to training Thanks to the power of modern hardware and software technology, learners can access the world of knowledge and information, search and receive information to master knowledge, practice skills to integrate into the current global environment The survey results on students’ feelings about learning, in general, show that they tend to express positive attitudes and behaviors towards learning; PSP have a more positive feeling about school than SSP and HSS

- Perceptions of teachers includes assessment questions about teaching style, teacher support, trust and sharing with teachers Research on the smart school model in the world has shown that the teaching methods used in the smart school model are smart teaching and learning strategies, called smart