1 The situation is the same in other regions, as reproducing 5 years old data without data discrepancy remains a major challenge, since education data at the ministry is managed manually which oftentimes are characterized with data overlap and data gap. Those issues are still at the front burner in recent times, many countries have performed poorly in rendition of annual education indicators into the UNESCO education database. However, a survey conducted by Asian Development Bank (ADB) in 2018 revealed that no EMIS in the Pacific Island Countries (PICs) appears to have progressed beyond the stages of collecting demographic data and reporting on sector indicators. EMIS is described as a smart system embedded with functions of sensing, self-inducing and controlling in analyzing education situation and decisions on available data in a predictive or adaptive manner. The design specifications and operations of Education Management Information System (EMIS) are within the Ministries of Education country’s objectives and priorities, yet the efficacy is still largely low. In spite of new contributions in IIS, there has been a growing demand for valid and reliable historical education data for planning, management of educational services and providing comparative information on pre and post-primary education performances in the developing countries. Cruz-Domínguez and Santos-Mayorga opined that IIS must possess the ability to efficiently store and retrieve large amounts of data needed to solve problems or make decisions. Intelligent Information Systems (IIS) which combines AI advancement, database technologies and knowledge to exhibit intelligent behavior in assisting users and other systems to retrieve or manipulate data becomes a game changer in allowing non-data analytics experts to make evidence-based decisions in complex situations. This advancement has uniquely separated plethora of information management systems from one another. Learning Analytics on Learning Management System (LMS) in tertiary institutions have also started receiving researchers’ attention because of its importance in improving students' academic performances and aiding decision in policymaking. Our solution renders dynamic visualized production-ready education data and 28 UNESCO standard indicators to guide decision making and this may serve as a model for PICs EMIS.Įducation sectors have received increase attention in recent years on Artificial Intelligence (AI) in the areas such as profiling and prediction, intelligent tutoring systems, assessment and evaluation, and adaptive systems and personalization. To solve these defects, we proposed data-driven microservices architecture developed with MERN (MongoDB, ExpressJS, ReactJS, and NodeJS) stack on 13 NoSQL collections, tested with pseudonymised data from Fiji Ministry of Education (consisting of 98.6% Learners, 100.2% Schools and 99.5% Teachers in post). Fiji EMIS which has prospect of leading other PICs EMIS could only generate few indicators manually, and the findings indicate that these indicators are not being considered in decision making. A SWOT analysis on the selected PICs EMIS through the published technical reports and policy documents from government and donors’ between years 20, revealed that EMIS in PICs have not progressed beyond the stages of collecting demographic data and generating basic indicators. This study examines the existing EMIS in the Pacific Island Countries PICs and proffers solution. Generation of quality data to aid planning, assessing the education performance, monitoring programs implementation and learning outcomes are the basic functions of the Education Management Information System (EMIS).
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