SHS Web of Conf.
Volume 92, 2021The 20th International Scientific Conference Globalization and its Socio-Economic Consequences 2020
|Number of page(s)||11|
|Section||Behavioral Economics and Decision-Making|
|Published online||13 January 2021|
Learning analytics for higher education: proposal of big data ingestion architecture
University of Pardubice, Faculty of Economics and Administration, Institute of System Engineering and Informatics, Studentska 84, 532 10 Pardubice, Czech Republic
* Corresponding author: firstname.lastname@example.org
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.
Key words: big data architecture / data ingestion / learning analytics / globalization / higher education
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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