1. Introduction
The increase in COVID-19 cases is affecting not only the health sector but also the economy, education and other sectors. COVID-19 has indirectly changed the landscape of the country’s education system. The government has issued regulations on the education and learning process at every level of education through the digital ecosystem and big data analytics technology. Here, the goal of education system management, especially during a pandemic, is to increase access, efficiency, effectiveness and quality through effective systems of monitoring, evaluation, budgeting and planning. This type of management system is called an Integrated National Education Information System (iNEIS
TM). iNEIS
TM is aimed at enabling this decision-making by providing data and information. Integrated data and information systems are at the core of iNEIS
TM development in their support of educational management functions throughout the education system (
Figure 1) [
1].
Looking into the fundamental concepts, the information system (IS) has facilitated the needs and requirements of decision-making not only at the managerial level but also at the operational level. On the other hand, the management information systems (MIS) have changed management discipline in the area of leadership, decision-making, workload, human resources management, communication, responsibility and planning. MIS can convert the collected data from the routine user and machine interactions into valuable information which will be used by the decision-makers to make efficient decisions. Furthermore, it is a collection of related components designed to support operations, management and decision-making through intelligent decision stages based upon the information derived from reliable data that aims at facilitating exchange and information sharing within an organization. Similarly, in the context of education, continuous monitoring and evaluation of the education system by collecting and examining data and information are used in the process of educational decision-making. Several surveys have been conducted in recent years to gather information on the extent to which schools are developing the capacity to integrate MIS into learning, teaching and management processes [
2]. Moreover, it was clear that an important feature to be considered was the relationship between data collection and collation to data use. School managers needed different forms of analysis compared to the analysis needed by teachers. A series of studies highlighted important features of computerized school and management systems; their implementation in a range of schools offers the widest view of ICT (Information and Communication Technology) from the perspective of MIS [
1,
3,
4,
5,
6,
7].
However, the aim of this study was to reveal the effectiveness of the newly implemented integrated iNEISTM and its implications for educational management that may be applied during the COVID-19 pandemic, especially to reveal and explain the benefits and challenges that hinder the effective implementation of iNEISTM to its full potential. It specifically examines the benefits of the system and also obstacles that hinder the efficiency and effectiveness of the system. This research implies that when both the stakeholder-provider and end-user understand the significance of iNEISTM, there is a great possibility that the implementation will succeed in harnessing the full value of the system. The study provides the stakeholder-provider with useful guidelines in the form of recommendations to be implemented to ensure improvement in the effectiveness of iNEISTM. The paper is structured in the following way. Firstly, a literature review describes Education Management Systems in detail. Secondly, the methods are discussed that were used in data collection along with the limitations that had been encountered. Thirdly, the key differences and similarities between uses of the terms are shown and discussed with support from findings in the literature that is related to research in the Integrated National Education Information System (iNEISTM). Finally, the results of the different views are discussed and some recommendations are also given in the concluding section.
3. Methodology
The triangulation research methodology was used in this study, which was a combination of both qualitative research and quantitative research [
53,
54]. While the qualitative research aimed to gather an in-depth understanding of the impact of the iNEIS
TM system, both from the developer’s and from the end-users’ point of view, the quantitative aspects explored the perceptions towards iNEIS
TM. There were 300 respondents who participated in the online survey questionnaire and it received quite overwhelming responses. Most responses were received through social media, i.e., Whatsapp and Facebook. The sample size is significant based on the confidence level of 95% and 5% margin of error for a population of around 8000 teachers of public schools.
Purposive sampling was used to focus on iNEISTM users and implementers, who represent a particular population that is of interest, which was appropriate to enable us to address the aims of this paper. The questionnaire survey mainly used closed-ended questions and one open-ended question for respondents to answer. In addition, a semi-structured face-to-face interview was adopted to enable us to gain in-depth information about iNEISTM usage experience. The survey responses were analyzed using descriptive statistics and were transformed into graphical charts, whereas the interviews were recorded in audio form, transcribed and transformed into written documents for thematic analysis purposes. The research applied several ethical considerations to maintain the validity and reliability of the data collected. All information and data from respondents were treated as strictly private and confidential; anonymity was maintained and consent obtained prior to survey and interviews.
4. Findings
The purpose of conducting the quantitative survey was to explore further the perceptions, opinions, issues and implementation of iNEIS
TM. From the findings, it is apparent that teachers from all over the nation participated in the survey and the largest group comprised teachers from the Brunei-Muara District 71%, Tutong 18.7%, Belait 8.7% and Temburong in the remaining 1.6% (
Figure 2).
4.1. User Experiences
The findings show that 16% of the respondents were teachers who had been in service for 16 years and above. This question was included in the survey to determine the relationship between the age factor and the behavior of older generations to accept change. As was highlighted in the survey, this group was the most resistant to change. Referring to the interview responses, the older/senior teachers in schools were the most reluctant people to use the new system. They showed no interest in learning how to use iNEISTM and required the junior teachers to help them. The user respondents of iNEISTM included teachers, education officers and also school leaders. “Education officers” were degree holders whilst “teachers” in this context were teachers who were diploma holders. School leaders were headmasters/headmistresses/principals of schools. More than half of the respondents were teachers from primary schools. There was no difference in the allocation of responsibility in the use of iNEISTM between primary and secondary schools. The significance of this question was to collect the different opinions and points of view of teachers from both primary and secondary schools. Although the responsibilities of teachers in all schools were more or less similar, the culture and working environment varied.
4.2. Workload Issues
The implementation of the new system, iNEISTM, was seen as an additional burden to teachers. From the literature, Sandy Britain, a specialist at Education Enterprise Architecture (EEA), said that the system would allow teachers to focus on teaching, and schools could update details through the system and would not need to search for information through spreadsheets. From the responses to our survey, this is not the case. The goal of iNEISTM was to minimize teachers’ overall workload, but we found otherwise. In total, 93.7% of the respondents, which was around 281 out of 300 teachers, claimed that the system did not help them at all. Although almost all the teachers underwent training before they started to use iNEISTM, only 21.3% (around 64 out of 300) of them fully understood the whole function of the system. There were even users who had no knowledge about the system at all. The neutral responses were assumed to have 50–50 knowledge. Some teachers stated in the survey that the reason some users were less committed to accepting iNEISTM might have been that there was no prototyping, and during training, there was no clear justification of the benefits of the new system.
4.3. Problem of iNEISTM
More than half of the participants (61%) agreed that the system (iNEISTM) was the main cause of the problem and internet connection was second (23%). This was mainly due to the system and internet connection being unstable most of the time. Moreover, since the implementation of the system, teachers had to fulfil “double jobs”. First, they were required to enter and update data in the system, and at the same time, they had to produce hard copy reports. This might also be due to the lack of users’ involvement during system development. The process of involving users in the system development is critical to the success of any development project. User involvement has played a prominent role in the success of system development efforts. Referring to the recent news in the mass media, UNN (Unified National Networks) is working on improving the internet connection and has announced that faster internet connection will be provided throughout the national coverage. On the other hand, according to the teachers during the interview, they had only undergone one initial training session before the implementation of iNEISTM. The majority, 70%, agreed that the training was inadequate. When the system was launched and first started to be used in schools, teachers were still uncertain about the whole system. The system was still ambiguous to them and they had to learn for themselves the functions and processes embedded in the system despite being unsure about it.
4.4. With or without iNEISTM?
This question intended to determine the users’ perceptions of iNEISTM as a tool to make work easier. Around 82% of the respondents still preferred to carry out their job using the traditional way, without using the newly implemented IS. Despite the “struggle” that most users faced in using the system, 10% of the respondents accepted that iNEISTM made it easier to complete their job. Lack of user involvement traditionally has been the major reason for project failure. Conversely, the main contributor to project success has been user involvement. However, among the most common comments and recommendations that were made by the respondents were the following:
- i.
The system needed lot of improvement in order to be an effective system.
- ii.
The main listed reason for this was the unstable internet connection and also the instability of the system itself. It was intended for the new system to replace the manual system, but now, with iNEISTM, both are needed.
- iii.
The system had a complicated interface, was not user-friendly, had too many technical terms and had too many components that tended to confuse users.
- iv.
Teachers should have been able to focus on teaching rather than spend a great deal of time on iNEISTM attending to the errors and complications of the system.
- v.
There was an ack of support from the iNEISTM team and helpdesk.
- vi.
The initial training was inadequate for teachers to master the system and to use it effectively.
- vii.
Internet connection should be improved for better and stable connection of the system.
- viii.
iNEISTM placed a greater burden on teachers in terms of both academic and non-academic tasks.
Even though most of the comments criticized the inefficiency of the system, a significant number of respondents did find iNEISTM to be a good system. A few accepted the system as a good one once they were familiar with it. Some suggested that more training, and awareness of the benefits of the system, would be beneficial, and more support from the iNEISTM team was required. Some respondents also requested for the interface of iNEISTM to be redesigned to be less complicated, more user-friendly and simpler. In essence, the purpose of iNEISTM is only to provide statistical figures. From these, other departments can generate reports and begin to analyze the data. It was discovered from the teacher’s perspective that a number of them prefer to use the traditional method and argued that the system was acceptable if no problems occurred. The findings further showed that there were barriers that made the system ineffective and inefficient. These barriers included the internet connection, the system itself and also the users’ attitudes towards change.
5. Discussion
Consolidation of our findings and analysis enabled the iNEIS
TM framework to be developed as shown in
Figure 3. The findings reveal that the responses provided by the teachers during the interview were similar to the comments that were written by other respondents who participated in the questionnaire. This created difficulty for the users (especially teachers) as it created double the amount of work for them, which might have affected their time for teaching. However, the respondents also believed that the system did have potential to be good and beneficial if only adequate necessity for its development was provided. Otherwise, the system would not work effectively and would increase the workload for the teachers as a result. This is in line with the authors of [
6,
7], who believe that MIS can provide administrators and teachers with the information required for informed planning, policy making, evaluation and workload reduction. It is claimed that MIS have changed school management in the areas of leadership, decision-making, workload, human resources management, communication, responsibility and planning.
Nevertheless, the main issue with the implementation of iNEISTM was people’s attitudes towards the system as most of them were reluctant to change. Some of the respondents from the questionnaire also mentioned that most users were very “closed-minded” about the implementation of the system. Meanwhile, according to the participants from the interview, some of the users were reluctant because some of them were computer illiterate. Perhaps, if more proper training was carried out, this would help the users to understand better how to use the system and thus more positive feedback from teachers could be obtained. This can be reflected by the theoretical grounding for TAM mentioned in the literature review. As mentioned in the literature review, TAM proposed two important determinants to analyze the factors which caused individuals to accept or reject information technology, namely perceived usefulness and perceived ease of use. The definition of “perceived usefulness” is the degree to which a person believes that using a particular system would enhance his/her job. This determinant may be ambiguous for this particular research. It seems that there were not many participants who considered that the use of the system could enhance his or her job performance, but they rather clarified that the system would usually create more work for them. However, “perceived ease of use” is the degree to which a person believes that using a particular system would be free of effort. This may be true considering that most participants in this research believed that the introduction of iNEISTM could minimize any issues when it came to completing certain tasks—they considered that the system could be useful as they believed that by using it, workload could be minimized. However, this did not occur in reality, as many issues arose once it was implemented. For example, the system became unstable and data were not synchronized, which made many users feel reluctant to use the system.
5.1. iNEISTM Big Data and Learning Analytics
Basically, learning analytics highly emphasize the measurement, collection, analysis and reporting of learners’ information with the aim of comprehending and maximizing the learning process as well as the environment in which it takes place. Learning analytics can be referred to as a series of experiments that develop ideas from different fields in terms of process mining, business intelligence, data processing, information retrieval and technology-enhanced learning. It is not solely based on technology or institutional statistics. The core of learning analytics is basically concerned with enhancing individual capabilities that will help individuals to achieve their potential and goals in life. In the big data era, learning analytics carry the possibility to offer educators information and tools that will help them in making decisions and encourage teachers to make full use of the technology through a range of learning concepts (
Figure 4). The incorporation of technology for learning purposes gives further information for analysis for supporting an ongoing cycle of continuous improvement. In this case, every educator should be given access to learning analytics tools and given proper training on how to use these tools properly. This can contribute to creating e-proficiency and support the development of targeted and effective learning methods. The strengths of learning analytics include the availability of a huge amount of educational data, the ability to use powerful, preexisting algorithms, the availability of multiple visualizations for staff and students, increasingly precise models for adaptation and customization for learning processes and increasingly valuable insights for learning strategies and behaviors. The opportunities of learning analytics include open-linked data able to enhance compatibility across systems, enhancing self-reflection and self-awareness, enabling the learning process to be carried out through intelligent systems and the feeding of learning analytics outcomes to another system to aid in making decisions. The threats of learning analytics include concerns regarding ethical issues, specifically with regard to privacy issues, over-analysis, insufficient generalizability of the outcomes, higher chances for misclassification of patterns and contradictory findings.
There is a correlation between big data and learning analytics. In numerous points of view within learning analytics, big data are linked to education. Learning analytics has been regarded as an educational application of big data. It basically consists of a branch of statistical analysis that was initially created as a method for businesses to conduct analysis in certain areas such as commercial activities, identifying spending trends and to making predictions regarding consumer behavior. In generic terms, big data expect the use of either information or database systems as the central storage facility as it is suitable for storing vast amounts of data and enabling certain transactions. For instance, the record-keeping framework of undergraduate students has the ability to store the information of undergraduate students in terms of their grades for each course they have taken. For institutional researchers, this kind of information is very useful and it can be used to analyze the performance of students over time. It could be analyzed from one semester to another semester or from one year to another year. In the context of the big data scenario, the information would be obtained for every undergraduate student’s transaction in every course, particularly if the course has been conveyed electronically online. This means that every undergraduate student’s data, basically on their course evaluation, discussion board section, blog section and any activity, could be recorded and this can produce a large number of transactions for each of undergraduate student per course. Moreover, this information would be gathered on the basis of real time or close to real time every time it is executed, and after this, it will be examined further to recommend the course of action that needs to be taken. The application of learning analytics has been growing rapidly in educational institutions in which the learning process is being carried out online and in hybrid settings. It has become a more standardized type of learning process for educational institutions. A specific kind of web tracking tool has been widely used in educational institutions in order to record student behaviors on online courses. The tracking tools not only record basic variables like how much time students spend on a topic but also more nuanced information that could provide evidence of a student’s critical thinking, synthesis and the depth of retention of the information after some time. The data of the specific behavior of the student will be added into the student-related information. In this knowledge-based era, the majority of educational activities such as e-learning, tests, quizzes and other activities are being carried out online. Nowadays, students and educators are increasingly connected to the internet because a significant amount of educational material is available in digital format. This proves that the availability of data sources on the internet for learning purposes has been expanding. There are several main sources used for learning analytics, such as the virtual learning environment (VLE), student information system (SIS), learning management system (LMS) and library system.
5.2. Student Information System (SIS)
The student information system is an important part of the education system, especially in this modern era. It is believed that this system could solve the problems or difficulties with using the traditional approach of gathering relevant information about students through the internet. Moreover, this system is designed to replace the paper-based system of collecting and recording student information, which is time-consuming and difficult to manage. The student information system consists of detailed information about students in terms of their prior qualifications, socio-economic status, ethnic group, module choices and grades achieved to date. From this system, there will be potentially valuable insights that could be used to design a virtual learning environment so that it will be easier to predict student academic performance in education settings (
Figure 5). The majority of student information systems are involved in administrative activities such as admissions, enrolment and examinations. It is clear that the use of student information systems can be beneficial for education institutions to record and maintain student information easily, coordinating different levels of student information, providing access to information by students, instructors and parents 24/7 and storing biometric system information. Besides the above, the system is user-friendly, ensures efficient services, minimizes the workload of the users and makes it easy to retrieve data.
5.3. Learning Management System (LMS)
The learning management system is widely recognized as the framework that manages every aspect of the learning procedure. The use of the framework of the learning management system supports the delivery and management of content and helps in identifying and evaluating individual or organizational learning or training objectives, tracks the progress towards these objectives and gathers and displays the overall information of learning procedure. It also aids in managing course registration, administration, analysis, tracking and reporting. The learning management system is the most common data source for higher educational institutions through an online portal that connects with the lecturers and undergraduate students. The learning process in higher educational institutions is no longer the same as in the primary and secondary school system, which is only a one-way learning process. In higher education systems, the lecturers usually provide lecture notes and extra information for the students to access on their own. This means that in the higher education system, students are encouraged to be exploratory learners throughout their studies. Moreover, throughout the lecture session, the lecturers will share their knowledge on certain topics and students participate by giving their own opinions or thoughts on the related topic during class discussion. Hence, university students need to continuously expand their knowledge by gathering more information. The advantage of using LMS for lecturers is that it is easy to manage a large number of students in the system. This will also save their time as the lecturers can easily identify and monitor their students. For example, the lecturer has three different lecture classes. With LMS, the lecturer can easily determine which students are taking his or her class based on which class the students are registered for. In this case, it will be easier for the lecturer to provide lecture material as well as receive assignments from the student. The drawback of using LMS is that it is difficult to enable active discussion with all the students—for example, when there are hundreds of students posting messages to a lecturer. It is difficult for the lecturer to reply the students’ messages specifically. This can be time-consuming and increase the stress level of the lecturer due to excessive messages.
5.4. Library System
Transforming the library into a cloud-based shared system can actually contribute to the success of learning analytics. A vast amount of data could be generated through the library system. Basically, the library system has the ability to record student information, especially when students borrow books from the library or when students access the library system to read electronic journals. This kind of information is very useful for analytics to study the number of expected students visiting the library and student behavior. There are a number of initiatives to enhance the library system in order to provide a future generation of learning tools. One of the initiatives is due to the fact that many educational institutions are looking for distinct collections of sources for teaching purposes. This is where the library system plays an important role as the system consists of diverse academic sources, including thesis papers, past year papers, presentation slides, articles, journals, reports and other sources. Moreover, the source of information used for virtual learning environments and learning management systems usually comes from the library system. The advantage of using a library system is that it is very easy to use. The students or lecturers can simply search for books through the system, without the hassle of going to the library to look for books manually. Not only this, but the system can be accessed 24 h a day and this makes it convenient for both students and teachers as they can access library sources at any time. Moreover, digital copies of the books, journals, thesis and other academic sources are available. Students and teachers can benefit from this as they do not have to carry heavy books from one place to another. The drawback of using a library system is that when there is no internet connection, the system cannot be accessed.
5.5. The Challenges of Learning Analytics
Learning analytics is said to offer greater opportunities in providing the best learning experience for students, especially in this big data era. However, there are a few challenges that need to be taken into account, especially the ethical issues that are likely to occur. One of the ethical issues of learning analytics that can be raised is mislabeling students based on incomplete or incorrect information or incorrect calculation. This factor needs to be considered because the ways in which learners behave either in classrooms or through the internet depend on the level of complexity of personal, emotional, social and economic factors that cannot be directly observable from their behavior alone. In this case, learning analytics may have lessened the effectiveness or limited the learners’ choices of access to specific materials or resources. For instance, the adoption of learning analytics could be useful to suggest courses and modules which might improve a particular student’s chance of completing their qualification successfully. There will be more likely students taking easy courses with the highest chances of success than those students who are taking courses with a high level of difficulty and more challenging content. In this case, it is essential to include students as active agents and collaborators. Through student-oriented learning analytics and the data being shown to the learners, it will give both lecturers and students the chance for self-reflection and the development of shared understanding. Another ethical issue of learning analytics involves legal issues—specifically, privacy concerns, security and the handling of personal information of the students and the teachers, as this information is needed as part of the educational data for learning analytics. Both teachers and students may raise concerns regarding the security of their personal information and who is likely to access the information about their ability and knowledge [
44,
55,
56]. These issues need to be addressed as the students and the teachers definitely require answers regarding the extent to which their personal information is going to be used, because it can be considered unethical if the information is misused and this can actually lead to violating the students’ and the teachers’ personal information. Apart from this, it should be highlighted how exactly learning analytics can enhance the learning process in educational settings. In this case, it is important to note certain criteria of learning analytics in terms of its reliability, validity, effectiveness and usefulness in generating learning outcomes. Moreover, it is important to consider how prepared are the education institutions for the adoption of learning analytics. This is because not everyone knows how to use big data and learning analytics properly. Without proper guidelines on how to effectively use the information, this might affect the performance of the students adversely. It is crucial to make preparations by having expertise on this area and changing the infrastructure in order to ensure effective use of learning analytics in learning processes.
One of the main ways that big data can support higher institutions or universities is through engagement between the students and the institutions. Education institutes have introduced a large number of students’ engagement points from big data which can help the students, starting from initial profiling all the way to their graduation from the institution. The student engagement lifecycle considers how the institutes influence and improve the student’s engagement and also how it could increase their time in the university through big data-powered applications.
5.6. Student Acquisition
Information about past performance and also data of both current and past students can be used to develop profiles that indicate which kinds of students are most likely to be registered in a particular institution. There is a need to employ graphic analysis to review their students’ social network to pinpoint their friends or acquaintances which could be the institution’s potential new scholars. The universities or institutions could use the students’ previous data, such as their high school results and performance and also some other tests that they completed, such as aptitude tests or surveys about their interest, and correlate these results with the universities’ graduates and be used to advise the students regarding which major or curriculum to take. It is also necessary to incorporate outsource data in regard to the future workforce competence and ability requirements and income, which can help the students to make the correct choice to decide which minor or major will be suitable for their future.
5.7. Student Performance Effectiveness
The students’ current test results are compared to their previous ones and similar students with similar grades are grouped together and monitored. The lecturer’s notes and social media data such as tweets, blogs, YouTube and hashtags should be incorporated in order to develop a much more precise profile of the individual’s weakness and attitudes. Creating individual tutoring or small group tutoring for those students who are having problems or even advising them to change their major could be recommended to help those students who are having trouble. The appendix section of this report shows an example of the student progress flow analysis.
5.8. Student Work Groups
To improve a student’s individual performance, the universities can arrange for a leverage cohort analysis which enables the students to work together both inside the class and also outside the lectures, which can help them to improve their individual achievements. This activity then enables the lecturers to identify the group’s assignment and the reasons and factors for those assignments and it also allows the lecturers to cancel. These analytics can reorganize the group task depending on outlined design components and also other factors in which the lecturers need to write down their conclusions, which are to be combined with the purpose of the group performance after reorganizing in order to amend the dataset. The appendix section shows an example of the student cohort analysis.
5.9. Student Retention
The next application is to incorporate past data and grades, which include their effectiveness and also their group works, which it then connects with respective demographic, social data and also financial in order to grade the possibility attrition and distribute suggestions, which enables the universities to reach a decision regarding whether to keep the student or not. Through distributing and measuring the success of a particular suggestion, it allows the lecturers to come up with their own suggestions, in which it could check for possible outcomes and can be practiced in forthcoming detention interference propositions.
5.10. Teacher Effectiveness
This application section concerns adjusting and measuring the lecturer’s achievements. Their achievement can be measured by the number of students, the students’ probabilities and their attitude categories, subject matter and also a few other variables which can certify that the lecturers have the best experience for the students and lectures. Refer to
Figure 5 for an example of teacher effectiveness analysis.
5.11. Student Lifetime Value or Booster Effectiveness
It is necessary to plan in advance with consideration of offering possible levels for both undergraduates and postgraduates. The major influence of targeting, profiling and messaging to improve postgraduate success is determined by understanding the possibilities of achieving new or forthcoming potential of income and prosperity. Individuals who take advantage of these are the initially recognized as forthcoming supporters.
5.12. Student Advocacy
Data from the individual’s social network or their grades are used to come up with a student advocacy score which can influence the student’s acquisition, in which it targets a successful student’s peers; retention, by flagging any adjustments which could be made to avoid retention complications; performance effectiveness, by flagging adjustments which could be made to prevent lecture room achievement complications; and lifetime value applications.
5.13. Bookstore Effectiveness
The last big data-powered application is the bookstore effectiveness, which involves the use of the retail industry’s finest proceedings to upgrade bookstore benefits using analytics-driven applications such as textbook stock optimization and also merchandising effectiveness.
6. Conclusions and Recommendations
In conclusion, there were more undesirable impacts to the end-users resulting from the implementation of iNEISTM. The surveys that were conducted showed that the users are still not ready to accept the organizational change. Management Information Systems have changed school management in the areas of leadership, decision-making, workload, human resources management, communication, responsibility and planning. The intended purpose of the system, to lessen the workload of teachers, was proven to be unfulfilled. Since iNEISTM is fairly a new system, it needs more time to be accepted. In comparison to other studies, likewise, the adoption of E-Government websites/services is significantly related to the quality of information systems.
A number of users cannot accept the system as it is seen to be an additional burden. ICT adoption and diffusion has been studied in great detail by researchers in the information system area. However, ICT acceptance in education remains a central concern of information systems research and practice. Although IT is playing an increasingly important role in contemporary education, resistance to IT remains significant in the education sector. Understanding the conditions under which ICT are or are not accepted and used continues to be an important issue. iNEISTM is not a failure but there are symptoms showing that the system is not being used effectively. Based on the analysis, this is due to a lack of some factors such as a culture of communication and information sharing.
Resistance to adopting a culture of information use can often be attributed to a lack of shared vision for system development—in this case, iNEISTM. A shared vision is one that is developed from individuals’ visions for iNEISTM—what it should be, how it should function, what goals it seeks, how it should be able to improve the education system for the common good. Without a shared vision, units and individuals within the ministry are less likely to feel ownership of the system and are less likely to be proactive in the advancement of iNEISTM. Although problems appear to be many and known to all, an analysis of these problems and the capability of identifying strategies for resolving these problems remains inadequate. System monitoring and evaluation is yet to be institutionalized; research and analysis is yet to be established. However, iNEISTM needs a clear vision to see and know what to produce, who the product is designed to support and which departments and units to include. The development of iNEISTM involves nurturing a new management culture more than establishing a data and information system. The process of data collection, integration, analysis and dissemination is important, but even more critical is the culture of data sharing, information use and organizational management that leads to the effectiveness of iNEISTM.