Abstract
Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.
Highlights
In this era of globalization, large amounts of data generated in every area of our lives due to the rapid development of new technologies such as the Internet, social media, Internet of Things (IoT), cloud, smart and mobile devices
The paper identified possible big data sources inside academic institutions. This design of data ingestion architecture for learning analytics was done based on the types of devices available for data collection and the different sources of data generated inside academic institutions
This proposal of data ingestion architecture was based on the guidelines and best practices of general big data architectures from works of literature and they are adopted based on guidelines
Summary
In this era of globalization, large amounts of data generated in every area of our lives due to the rapid development of new technologies such as the Internet, social media, Internet of Things (IoT), cloud, smart and mobile devices. Higher education institutions are generating multiple formats of data from diverse sources across the globe. The use of analytics in an academic institution is in its infancy [2] in comparison with other business sectors, and the potential for data analytics to impact higher education is growing. There is a growing interest in data analytics at higher education [3] to improve the performance of students; to predict enrolment forecasts; to detect early dropouts and provide targeted interventions to help them remain in the university system, and effectively utilize academic resources [4, 5]. Universities can benefit from big data to enhance the effectiveness of academic faculty and reduce administrative workload [6]
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