Article 2

Baku, Azerbaijan| 107
INTERNATIONAL JOURNAL of ACADEMIC RESEARCH Vol. 4. No. 4. July, 2012
S. Omidinia, M. Masrom, H. Selamat. Adopting ICT for interactive learning: smart school case in Malaysia.
International Journal of Academic Research Part B; 2012; 4(4), 107-115. DOI: 10.7813/2075-4124.2012/4-4/B.16
ADOPTING ICT FOR INTERACTIVE LEARNING:
SMART SCHOOL CASE IN MALAYSIA
Siavash Omidinia
1
, Maslin Masrom
2
, Harihoddin Selamat
3
1
Centre for Advanced Software Engineering, International Campus Jalan Semarak, 54100 Kuala Lumpur,
2
College of Science and Technology, International Campus, Jalan Semarak, 54100 Kuala Lumpur,
3
Faculty of Computer Science and Information System, Universiti Teknologi Malaysia (UTM)
81310, Skudai, Johor (MALAYSIA)
E-mails: Siavash_omidinia@yahoo.com,maslin@ic.utm.my, harihodin@utm.my
DOI: 10.7813/2075-4124.2012/4-4/B.16
ABSTRACT
Computer assisted learning in educational institutions is one of the priority areas aimed to help students to
adopt Information Communication Technologies. Using ICT’s for interactive learning require ICT infrastructure
including updated software’s, multimedia systems and courseware’s, computer laboratories and literate staff. In
current information age, systematically reinvented smart schools are considered channel for improved teacher
learning practices as well as promoting awareness about multimedia technologies among students. The aim of this
paper is to evaluate the computer assisted learning among smart school teachers as well as students. Data was
collected from ‘Selangor’ state of Malaysia using case study approach. Data was collected from 14 smart schools
using random sampling technique. Results indicated that use of ICT and multimedia technologies are successful
contributors towards smart schooling success. Most of the teachers prefer to use multimedia technologies for
improved computer assisted education and interactive learning. Students believe that use of ICT’s and multimedia
technologies in school improved their interest toward usage of computer and internet. Though few teachers have
personal web pages but most of the schools have internet, computer laboratories and prefer internet based
assignments. It is suggested that for improved computer assisted education, schools were provided with updated
software’s, computers and knowledge. Ministry of ICT have to allocate a portion in financial budget to continue
computer based learning in smart schools. There is need to improve number of listed smart schools in country to
get benefit from information era.
Key words: Computer Assisted Education, Interactive learning, smart schools, ICT infrastructure, Multime-
dia technologies
1. INTRODUCTION
The emergence and evolution of the internet technologies leads towards development of useful and interact-
tive tools, as a result computer based learning is becoming increasingly important and is making the learning
process more easy and effective in many contexts. With the advancement in Information and Communication
Technologies (ICT), numerous new techniques are adopted by educational institutions. Change in teaching
methodology with usage of ICT not only improves the learning methods, but also change the conception of
teaching as well (Robin, 2009).
Information and communication technology (ICT) is an increasingly powerful tool for participating in global
markets; promoting political accountability, improving the delivery of basic services and enhancing local
development opportunities. The rapid development of (ICT) affects the development of technology implementation
in every aspect of life, from business, entrainment, socials and education especially. The benefit of ICT’s role in
education had been realized since 1990’s, along with the declining price of personal computer, the widespread of
internet access, the increasing of information technology( IT) application in the documentation functions, and the
availability of animation software development (Ong and Lai, 2006).
In order to take advantage of new technologies, there is need that developing countries should transform
their education system under the guidance of their national education policy. Education system must be creative,
thought provoking and informative rather than memory based. Malaysian National Philosophy of Education states
that “developing the potential of individuals in a holistic and integrated manner, so as to produce individuals who
are intellectually, spiritually, emotionally and physically balanced and harmonious”. Malaysian vision 2020 also
includes ICT Master Plan 2001, which focus on use of ICT in education especially in three major areas: (1) reduce
the digital gap by creating awareness of ICT among students in schools (2) ICT must be used as teaching and
learning tool in class rooms (3) improve management system efficiency through ICT (Rohani, 2002).
Limited developing nations in Asia are successful in implementing ICT framework in schools. Malaysia is
among one of the successful countries, who promoted education through ICT using smart school practices.
Malaysian government had initiated smart school system since 1996. In every state of country, there are number of
smart schools having ICT framework for students. Ministry of education reserved almost 30 percent of annual
budget for connecting rural areas with internet. Smart school system is working in country from more 15 years. The
objective of this paper is to analyze the determinants of smart school system success in Malaysian.

108 | PART B. SOCIAL SCIENCES AND HUMANITIES
2. REVIEW OF LITERATURE
The idea of the smart school is actually to revolutionize the education system through a holistic approach to
a development that focuses on the individual, making value based education available to anyone, anytime and
anywhere. The smart school vision brings together the vital components required to exploit technology to improve
the system and delivery of education to our children, and to achieve the objectives of improving technology
awareness among workforce; preparing work force for information age; democratize education and increase
opportunities to improve individual strengths and abilities (Strategic Plan for Smart School, 2005).
The smart school is a learning institution that has been systemically reinvented in terms of teaching-learning
practices and school management in order to prepare children for the Information Age (The Malaysian Smart
School Implementation Plan, 1997). A smart school will evolve over time, continuously developing its professional
staff, its educational resources, and its administrative capabilities. This will allow the School to adapt to changing
conditions, while continuing to prepare students for life in the Information Age. To function effectively, the smart
school will require appropriately skilled staff, and well-designed supporting processes.
Smart school system atmosphere encourages an active thinking process. For example, a new teaching
methodology must be developed to facilitate discussions, probe questions, encourages students to think and
stimulate creativity. In addition, this methodology must encourage students use personal computers (PCs), the
Internet and intranets as research and communication tools (Koshan, 2007).
Abas (2003) identified that there are there are three main concepts of smart schools need to be applied to
the domains of professional responsibilities of teachers. Significant changes in professional practice will only occur
when these concepts are effectively translated into the professional development of teachers. He suggested that
these three concepts include appropriate use of technology, thinking and creativity enhancement and value
inculcation.
Appropriate use of technology to enhance learning among students is major responsibility of teachers in
smart schooling system. Technology is used as a tool and should be integrated into the curriculum rather than be
taught separately as an end in itself. It is best learned within the context of meaningful tasks. Similarly, improving
thinking and creativity among students is another bright aspect of smart school. Towards achieving this, teachers
have to allow the students to determine for themselves when and how they learn. Teachers must have to learn
methodologies including student-centred instruction, team teaching, and interdisciplinary project based instruction
and individually paced instruction. Similarly, usage of interactive multimedia, internet and co-operative learning
skills can help teachers in increasing student interest towards new technologies. While teachers can inculcate
value by developing and improving students sound moral reasoning skills to reach the higher stages of moral
development. Teachers need to integrate activities that focus on moral development which include forming values,
organizing a value system and developing consistent philosophy of life (Abas, 2003).
2.1. Smart School technology drivers in Malaysia
Technology alone will not make a school smart. Only improved teaching-learning strategies, management
and administrative processes, and capable, well-trained people with enthusiasm for their work can do that.
However, information technology can enable the process of transforming traditional schools into smart schools.
Consequently, a nation-wide system of smart schools will depend on advanced information technology at the
school, district, and national levels. Technology has many roles to play in a smart school, from facilitating teaching
and learning activities to assisting with school management. Fully equipping a school might include the following("A
Smart School in Action, MSC Success Stories," 2002):
 Classrooms with multimedia courseware and presentation facilities, and e-mail or groupware for
collaborative work.
 Library/Media Centre with a database centre for multimedia courseware, and network resources
like access to the Internet.
 Computer laboratory for teaching, such as Computer Studies as a subject, and readily accessible
multimedia and audiovisual equipment.
 Multimedia Development Centre with tools for creating multimedia materials and catering to varying
levels of sophistication.
 Studio/Theatre with a control room for centralized audiovisual equipment, videoconferencing
studio, and preview room for audio, video, or laser disc materials.
 Teachers’ Room with on-line access to courseware catalogues and databases, information and
resource management systems, professional networking tools, such as e-mail and groupware.
 Administration Offices capable of managing databases of student and facilities, tracking student
and teacher performance or resources, and distributing notices and other information electronically.
 Server Room equipped to handle applications, management databases, and web servers; provide
security; and telecommunications interface and access to network resources.
Figure-1enlist some useful technologies adopted by Malaysian schools. Implementing smart schools
successfully will be a complex task, requiring changes to existing policies, procedures, and practices, both written
and unwritten. It may also require formulating entirely new policies and regulations. A few of the important issues to
be addressed include in the areas of the teaching-learning processes; management functions; people, skills and
responsibilities; and technology.

Fig. 1. List of Some technologies used in smart schools
The ‘brain’ of the solution, the Smart School Management System or SSMS (Figure 2) has a comprehensive
application software system developed to facilitate efficient and effective management and administration of
resources and processes required to support the teaching and learning functions of the education institution. It is
designed with a browser based user interface and is able to operate both on a local and wide area network
infrastructure.
Fig. 2. Smart Schools Features
2.2. Smart school system success factors
Transforming traditional schools into smart schools represents a major undertaking. It will require a
significant commitment of resources, but the nation will benefit from the change for many years to come. Success
of smart school system require (Strategic Plan for Smart School, 2005):
 support from many stakeholders, including all agencies in the educational system;
 sufficient funds to establish and maintain smart schools;
 appropriate policies, norms, and guidelines to support the schools;
 effective and efficient administrative practices in each school;
 sufficient deployment of information technology to enable the smart schools to function properly;
 Continuing professional development for teachers, principals, and other educational personnel.
Andersson and Grönlund have been studied and analyzed several related papers regard to e-learning
activities in different developing countries and finally they developed a conceptual framework for e-learning

(Andersson & Grönlund, 2009). They discussed on challenges of e-learning in developing countries and they found
30 challenges and summarize them in four categories: courses, individuals, technology and context.
Table 2.1. Conceptual framework of challenges in e-learning
Categories Sub-Groups Challenges
Individual Student
 Motivation
 Academic confidence
 Technological confidence
 Social support (support from home and employers)
 Gender
 Age
Teachers
 Technological confidence
 Motivation and commitment
 Qualification and competence
 Time
Course Course Design
 Curriculum
 Subject content
 Teaching and Learning Activities
Support Provided
 Support for students from faculty
 Support for faculty
Contextual Organizational
 Knowledge management
 Economy and funding
 Training of teachers and staff
Social /Cultural
 Role of teacher and student
 Attitudes on e-learning and IT
 Rules and regulations
Technological  Access
 Cost
 Software and interface design
 Localization
Adapted from Andersson & Gronlund (2009)
They stated that “The overall conclusion of these challenges are equally valid for both developed and
developing countries; however in developing countries more papers focus on access to technology and context
whereas in developed countries more papers concern individuals”(Andersson & Grönlund, 2009). Because
challenges are interrelated, based on their findings a conceptual framework of emerging issues for e-learning in
developed and developing countries was provided by them. They suggest that this framework (Table 2.1) can be
useful to guide both practice and research.
2.3. Determinants of smart school success
Andersson & Gronlund (2009) highlighted some challenges of e-learning in smart school system. These are
key issue which contribute in smart schooling success as well. Halim et al., (2005) suggested four parameters
which contribute in success of smart schooling system. These four parameters are curriculum, pedagogy,
assessment and teacher-learning materials.
2.3.1. Curriculum
The smart school curriculum shall be meaningful, socially responsible, multicultural, reflective, holistic,
global, open-ended, goal-based, and technological. It shall promote holistic learning, allowing children to progress
at their own pace, and catering for students’ varying capabilities, interests and needs. It will seek to ensure that
children are educated with critical and creative thinking skills, inculcated with appropriate values, and encouraged
to improve their language proficiency. Thus, the curriculum will be designed to help students achieve overall
balanced development; integrate knowledge, skills, values, and correct use of language; state explicitly intended
learning outcomes for different ability levels; offer multidisciplinary, thematic, and continuous learning; Foster the
knowledge, skills, and attitudes appropriate for success in the Information Age (Jaafar, 2008).
2.3.2. Pedagogy
The smart school pedagogy will seek to make learning more interesting, motivating, stimulating, and
meaningful; involve the children’s minds, spirit, and bodies in the learning process; build basic skills to prepare
children for greater challenges over time; and cater for a range of needs and capabilities among the students. The
pedagogy shall use an appropriate mix of learning strategies to ensure mastery of basic competencies and
promote holistic development; accommodate individual different learning styles, so as to boost performance and
foster a classroom atmosphere that is compatible with different teaching-learning strategies. Key features of good
online resources, from a student’s perspective, include (BackroadConnectionsPtyLtd, 2003b); accessibility (fast to
download, easy to read, easy to navigate), use of appropriate online features and good content design, i.e. the
learning content is enhanced by careful selection of appropriate and current learning resources; the contents are
motivational; the learning material is clearly presented and accurate, and appropriate level of instruction given.
Similarly, the content is written in appropriate style and format for online, and is complemented with downloadable,
printable material (NCVER 2002).

2.3.3. Assessment
The smart school’s assessment system will be distinctly different from current systems to help realize the
National Philosophy of Education It shall be element-based and criterion-referenced to provide a more holistic and
accurate picture of a student’s performance. Teachers, students, and parents will be able to access on-line
assessment items. Smart school assessment will be flexible and learner-friendly, while assuring the quality of the
assessment information by using multiple approaches and instruments. It will lead to living certification, which will
not only show to a student’s cumulative accomplishments but will also be open to continued improvement on a
lifetime basis.
2.3.4. Teacher Learning Material
Smart schools will need teaching-learning materials designed for the new teaching strategies. These
materials will accommodate students’ differing needs and abilities, resulting in fuller realizations of their and
potential, and allow students to take greater responsibility for managing and directing their own learning.
2.3.5. Environment
School environment is another crucial factor to facilitate learning process. Environment must be supportive
and encouraging which provide learning opportunity to both students and teachers. There is need that teachers
must be equipped and awared about latest technologies. Schools have to update teachers through learning
workshops and conferences. It facilitates teachers to convey updated knowledge to students. Similarly, awareness
about new software’s and latest technologies help students to fulfil the requirement of market needs.
3. MATERIALS AND METHODS
There are sixteen states in Malaysia. The data of this research was collected from one state i.e. Selangor.
Selangor is very near to capital city Kualalumpur. There are total 88 registered smart schools in Malaysia, while
data was collected from only 14 selected schools of Selangor state. Participants include both males and females
students as well as teachers. Total of 211 questionnaires were distributed among teachers and 430 among
students. Questionnaires were personally administered by researchers and contain assessment information about
curriculum, environment, assessment, challenges and opportunities for career.
3.1. Descriptive Statistics
Analysis of teachers shows that most of the female teachers (84.4%) are teaching in smart school, while the
percentage of male teachers is only 15.2 %. In smart schools, teachers belong to three ethnicities i.e. Malaysia
(84.8%), Chinese (8.5%) and Indians (5.7%). Almost all the teachers in smart school are young and experienced.
Almost above 60 % of respondents belong to age group of 30-50 years of age. Almost 83.4 % of respondents hold
degree as their qualification, while only 9.5 % of staff has master degree.
Table 2. Demographic information of respondents
Teachers
Categories Items Percentage
Gender Male
Female
15.2
84.8
Ethnicity Malaysian
Chinese
Indian
Others
84.8
8.5
5.7
0.9
Age 20-30 Years
30-40 Years
40-50 Years
> 50 Years
27
35.1
30.8
7.1
Education Certificate
Diploma
Degree
Master
Others
1.9
4.7
83.4
9.5
0.5
While in case of students, data was collected from starting year 1 to year 6. Most of the students in sample
belong to Year 3 (43.9 %), Year 4 (20.6%), Year 5 (14.8%), Year 6 (4%), Year 2 (11.1%) and Year 1 (5.3%).
3.2. Instrumentation
In case of student, questions were asked about computer based education, environment, challenges,
curriculum assessment and skills. In computer based education, students were asked about their interaction with
computer as general and usage of different Microsoft packages including Microsoft word, Excel and power point.
Environment deals with internet usage by students in hours at school and home and advantage which have due to
usage of internet at home on other students. Challenges to students were recorded by asking them about old
computers in laboratories, untrained teachers, guidance, class size and unavailability of updated software’s. In

curriculum assessment, students were asked about ‘how teachers assess them in computer based subject’ and
usefulness of contents taught in computer classes; while in skills for employment, students perception about future
employment with respect to computers were recorded.
4. RESULTS
The result section is divided in to two portions i.e. student results and teacher results. Student results
section contain information about computer based education, environment, challenges, curriculum assessment and
skills; while teacher section contain information about their interest and training, curriculum assessment,
challenges, environment and career.
4.1. Student Results Evaluation
Table-1 shows the total number of observation, mean values, standard deviation and standard mean error
for all the variables. Results shows that mean value for success is high and most contributing variable in success is
Interest and training curriculum assessment and school environment. The mean value of success is 60.8891,
interest and training is 23.0551, curriculum assessment is 12.911, challenge is 7.44, environment is 9.917 and for
career mean value are 7.55.
Table 1. Descriptive Statistics of Major Variables
Variables Total Sample Mean Score Std. Deviation Std. Error Mean
Success 211 60.8891 4.72662 .32539
Interest & Training 211 23.0551 3.69149 .25413
Curriculum Assessment 211 12.9119 2.04911 .14107
Challenges 211 7.4457 1.37673 .09478
Environment 211 9.9171 1.49847 .10316
Career 211 7.5592 .89716 .06176
Table 2 shows the results for β coefficient as well as for T-test. Results shows that career (t = 122.39, P <
0.05), environment (t= 96.34, P< 0.05), challenges (t= 78.56, P< 0.05), curriculum assessment (t= 91.53, P< 0.05)
and interest and training (t=90.72, P< 0.05). Value of T-test is significant for all the variables as significance value
for all the tested value is under 0.05 i.e. less than that of confidence interval. It means all the variable has positive
effect on success of smart schooling system, so we accept the entire null hypothesis and reject alternate
hypothesis. Moreover, the value of β coefficient (39.71) is higher than that of standard error (0.764), which is again
strong evidence to accept all H0.
Table-2 T- Test Results
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 39.717 .764 27.563 .00
Career 1.000 0.55 .190 122.390 .00
Environment 1.159 .043 .317 96.134 .01
Challenges 1.003 .046 .291 78.560 .03
Curriculum Assessment 1.129 .030 .434 91.531 .02
Interest & Training .918 .017 .781 90.721 .00
a. Dependent Variable: Success
α = 0.05
Table-3 shows the summary of regression results. In regression model, R-square provides statistics about
goodness of fit of model. It is also called coefficient of determination which measure how well the regression line
representing the real data set. A value of R-square equal to 1.0 indicates that the regression line is perfectly for
with the data set and proposed model is perfectly accurate to predict the scenario. While Durbin-Watson test is a
statistical test used to determine the presence of auto-correlation in data set. If the value of Durbin-Watson test is
less than 1, it means data has co linearity problem. Table results show that value for R-square is 0.753, which
shows that model explain 91% of variability. It means that model is reliable as predictors i.e. interest and training,
challenges, career, curriculum assessment and environment explained the dependent variable successfully.
Similarly, value of Durbin-Watson test is satisfactory and above 1, which mean that model has no auto-correlation
problem.

Table-3 Model Summary
b
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 0.868
a
0.753 0.751 2.35893 1.8
a. Predictors: (Constant), interest &Training, Challenges, Career, Curriculum Assessment, Environment
b. Dependent Variable: Success
4.2. Teacher Results Evaluation
Table-4 shows the total number of observation, Mean, Standard Deviation and Standard error for Student
data for smart schooling system. Mean observed value for success is 58.475, computer based education (25.75),
Environment (15.95), challenges (7.36), curriculum assessment (5.227) and skills (4.170), while the values for
standard deviation for success is 9.5 and standard error is 0.4622.
Table-4 Descriptive Statistics of Major Variables
Variables N Mean Std. Deviation Std. Error Mean
Success 430 58.4752 9.58631 .46229
Computer Based Education 430 25.7543 6.40022 .30865
Environment 430 15.9535 2.54428 .12270
Challenges 430 7.3690 1.61685 .07797
Curriculum Assessment 430 5.2279 2.18475 .10536
Skills 430 4.1705 1.35486 .06534
Table 5 shows the results for β coefficient as well as for T-test. Results shows that computer based
education (t = 83.44, P = 0.00 < α = 0.05), environment (t= 130.0, P = 0.00 < α = 0.05), challenges (t= 94.10, P =
0.00 < α = 0.05), curriculum assessment (t= 49.62, P = 0.00 < α = 0.05) and skill (t= 63.83, P = 0.00 < α = 0.05).
Value of T-test is significant for all the variables as significance value for all the tested value is under 0.05 i.e. less
than that of confidence interval. It means all the variable has positive effect on success of smart schooling system
as reported by student response results, so we accept the entire null hypotheses and reject alternate hypotheses.
Moreover, the value of β coefficient (36.88) is higher than that of standard error (1.330), which is again strong
evidence to accept all H0.
Table-5 T-Test Results
Model Un standardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.688 1.130 .849 .02
Computer Based Education 1.054 .011 .704 83.443 .00
Environment 1.027 .027 .273 130.024 .00
Challenges .917 .030 .209 94.510 .00
Curriculum Assessment 1.091 .041 .184 49.621 .00
Skills .897 .048 .141 63.831 .00
a. Dependent Variable: Success
α = 0.05
Table-6 shows the summary of regression results. In regression model, R-square provides statistics about
goodness of fit of model. It is also called coefficient of determination which measure how well the regression line
representing the real data set.
Table-6 Model Summary
b
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .956
a
.914 .914 2.81078 1.778
a. Predictors: (Constant), SKILL, CURASSESS, Environment, Challenges, Computer Based Education
b. Dependent Variable: Success

A value of R-square equal to 1.0 indicates that the regression line is perfectly for with the data set and
proposed model is perfectly accurate to predict the scenario. While Durbin-Watson test is a statistical test used to
determine the presence of auto-correlation in data set. If the value of Durbin-Watson test is less than 1, it means
data has co linearity problem. Table-6 results shows that value for R-square is 0.914, which shows that this model
explain 75% of variability. It means that model is reliable as predictors i.e. interest and training, challenges, career,
curriculum assessment and environment explained the dependent variable successfully. Similarly, value of Durbin-
Watson test is satisfactory and above 1, which mean that model has no auto-correlation problem.
5. DISCUSSION
Smart schooling system is newly established concept, support ICT framework for improvement in learning.
Teachers are considered as key actors in smart schools for delivering knowledge to students. Results show that
smart schooling system in Selangor paying less attention towards teachers training and development course.
Reported teachers participation in development activities including workshops, teachers club, Internship programs,
teacher resource centres and conferences is high. This means that administration of smart school is keen in
professional development of their staff. Most of the teachers in schools are interested to excel their expertise in
computer science courses. Most of them are interested in learning web designing, graphics, network application,
databases and operating system including their expertise in hardware. Most of the teachers are interested in online
and web based learning rather than course based learning. School administration has to deploy such trainings
which enhance teacher’s expertise in these mentioned specific areas.
Using ICT framework makes learning easy and effective. Most of school teachers are using different
computer based applications in delivering knowledge to their students. Usage of different computer based
applications according to preference is power point course slides, spread sheet, excel sheet, desktop publishing,
network management applications and interactive video session especially for mathematics courses. In few
schools class size is less than 20 students in class, while few school teachers reported class size of 40. Small
class size help teachers to concentrate on every student development, while in large class, teachers mostly
discuss on course contents rather than student development. It is important for people in administration including
staff and principles in smart schools to acquire computer related skills which help them to update their schools
according to the required standards of smart schools. Findings of previous researchers also highlighted that
teachers are not aware, how and which technologies they have to integrate for different subjects. Integrating
technologies help to improve learning process of students and increase their mental strength towards adopting new
technologies.
There are few limitations in smart schools which need to be highlighted for better progress of these schools.
Although students are interested to learn internet but few teachers have observations about student abuse use of
internet. Most of the teachers give internet based assignments and consider this as double learning source i.e.
expertise in computer usage and searching required topic. Providing such opportunities to students help them to
enhance their critical thinking as well as their self directed learning; it increase their motivation and willingness to
work independently at their work place.
6. FUTURE IMPLICATIONS
There is need to define some key rules for smart school project team which help participants to better
contribute in success of smart schooling system. All the participants in project team will be provided with
opportunity to share their visionary ideas with provide solutions to smart system problems. This will helps in
improving high inspiration among team members as well as help in learning leading concepts and comprehensive
course standards for smart schools. Such collaborative work help in attaining world class standards in smart
school. Teachers will have to play their role of guide such as they will identify goals, define direction for their
students, match this progress with defined goals and adopted a step back methodology, therefore provide students
with greater opportunity to learn at their own space. Teacher side by side check the students progress periodically,
identify weaknesses and decide the future course of action for their students.
REFERENCES
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2. 2002. A Smart School in Action, MSC Success Stories [Online]. Available:
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3. 2005. Strategic Plan for Smart School. Tehran: Ministry of Education, Iran.
4. A.H, R. 2002. Education reform to meet the challenges of a k-economy: The Malaysian perspective.
International Conference on the Challenge of Learning & Teaching in a Brave New World: Issues &
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Countries: A Critical Review Of Research Challenges. The Electronic Journal on Information Systems
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7. JAAFAR A. 2008. Malaysian smart school courseware usability study: the effectiveness of analytical
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8. KOSHAN H. 2007. Multimedia School, Steps toward smart school. Tehran: Tarbiat Moalem.
9. ONG C. S. & LAI J. Y. 2006. Gender differences in perceptions and relationships among dominants of
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