دانلود مقاله نظارت بر کیفیت در موسسات آموزش عالی
خلاصه
1. معرفی
2. پس زمینه
3. داده های بزرگ و تجزیه و تحلیل در آموزش عالی
4. معماری های هوش تجاری در آموزش عالی
5. پیشنهاد معماری هوش تجاری در آموزش عالی برای چالش کلان داده
6. توسعه داشبورد هوش تجاری برای مقابله با چالش کلان داده
7. مراحل توسعه داشبورد
8. بحث در مورد ویژگی های داشبورد نظارت بر کیفیت
9. نتیجه گیری
اعلامیه منافع رقابتی
منابع
Abstract
1. Introduction
2. Background
3. Big data and analytics in higher education
4. Business intelligence architectures in higher education
5. Proposed business intelligence architecture in higher education for the big data challenge
6. Development of the business intelligence dashboard dealing with the big data challenge
7. Dashboard development phases
8. Discussion of quality monitoring dashboard features
9. Conclusion
Declaration of competing interest
References
چکیده
از آنجایی که کلان داده ها در هنگام ساختن یک سیستم هوش تجاری (BI) به چالشی آشکار تبدیل می شوند، انگیزه ای برای رسیدگی به این موضوع چالش برانگیز در مؤسسات آموزش عالی (HEI) وجود دارد. کیفیت نظارت در HEI شامل مدیریت حجم عظیمی از داده ها از منابع مختلف است. این مقاله کلان داده ها را بررسی می کند و موارد از ادبیات مربوط به تضمین کیفیت (QA) در HEI را تجزیه و تحلیل می کند. همچنین چارچوبی را ترسیم می‌کند که می‌تواند به چالش کلان داده در HEI برای مدیریت نظارت بر کیفیت کیفیت با استفاده از داشبوردهای BI بپردازد و یک داشبورد نمونه در این مقاله ارائه شده است. داشبورد با استفاده از یک ابزار استفاده برای نظارت بر کیفیت کیفیت در HEI برای ارائه نمایش‌های بصری داده‌های بزرگ توسعه داده شد. داشبورد نمونه اولیه به ذینفعان امکان می دهد تا مطابق با استانداردهای QA را نظارت کنند و در عین حال به چالش کلان داده مرتبط با حجم قابل توجهی از داده های مدیریت شده توسط سیستم های QA HEI رسیدگی کنند. این مقاله همچنین نحوه ادغام کلان داده ها از رسانه های اجتماعی را در داشبورد نظارت توسط سیستم توسعه یافته بیان می کند.
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.