Abstract

As big data becomes an apparent challenge to handle when building a business intelligence (BI) system, there is a motivation to handle this challenging issue in higher education institutions (HEIs). Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources. This paper reviews big data and analyses the cases from the literature regarding quality assurance (QA) in HEIs. It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper. The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data. The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’ QA systems. This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Introduction

Quality assurance (QA) in higher education (HE) is emerging as the higher education institutions (HEIs) find it difficult to monitor their levels of quality of services. Governments impose national QA standards in order to achieve the minimum level of quality of services provided by these institutions. In the Kingdom of Saudi Arabia (KSA), Education and Training Evaluation Commission (ETEC) imposes, through its National Centre for Academic Accreditation and Assessment (NCAAA), the QA standards that all HEIs in the KSA are required to comply with for accreditation [1]. To measure compliance with these standards, in 2018, NCAAA developed 23 key performance indicators (KPIs). In 2022, NCAAA introduced a revised version of KPIs. This revised version comprises a total of 17 KPIs, and all HEIs are mandated to monitor and report on compliance. This evaluation process of compliance with these KPIs should take place during the forthcoming accreditation cycle for HEIs [2].

In addition, the increasing amount of data generated from traditional sources and through social media plays negative influence on business intelligence (BI). There is a desire to build low-cost data platforms that will be able to handle this increasing amount of data. This is sometimes known as the big data challenge [3,4].

In this paper, different BI architectures are presented. These BI architectures encompass different BI technologies that HEIs may adopt, taking into consideration their capabilities and specific requirements of HEIs. A proposed architecture is also presented to show how the big data challenge can be addressed to deal with huge amounts of data that the QA system might require particularly if the data is being linked to social media. The recent trend of measuring students’ satisfaction is through students’ comments posted on social media.

Conclusion

This paper showed how the HF-HEQ-BI framework can be applied to develop a BI prototype dashboard for monitoring quality in HEIs and a dashboard has been developed from the framework. The paper has also outlined different BI architectures that HEIs may adopt to handle the big data challenge while monitoring QA. These architectures show different configurations of BI systems that HEIs may adopt based on their requirements and available resources. The development process of the BI dashboard was discussed to show how HF-HEQ-BI FUT may be used to determine the requirements to be presented in the dashboard. HF-HEQ-BI FUT assists in determining the required visualisation for each KPI to be displayed in the dashboard. FUT is customisable and can be adapted by users to reflect the requirements of individual HEI. FUT shows how decision-makers in HEIs may select various visualisation dashboards while monitoring QA compliance regarding NCCAA standards.

A prototype dashboard was developed to illustrate the application of the framework and also to evaluate the HF-HEQ-BI framework. The developed dashboard shows a monitoring display of the mandatory NCAAA KPIs, institutional specific KPIs, additional KPIs, and social media analytics. The social media analytics presented in the dashboard included the sentiment analysis of data drawn from Twitter through Twitter API. Additionally, the proposed dashboard allows the dashboard users to represent additional KPIs related to performance monitoring in the institutions.

The developed dashboard based on the HF-HEQ-BI framework shows how data retrieved from social media is presented in summarised diagrams to handle the big data challenge. The prototype dashboard is dynamic and KPIs can be added or modified easily based on the changes in regulations or institutional directions of the strategic plans. Our future work will focus on the evaluation of the proposed dashboard to meet the big data challenge.