Learning analytics for higher education: proposal of big data ingestion architecture

Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The ingestion layer is responsible for collecting and transform analysis. Learning analytics plays a vital role in providing decision-making support selection suitable timely intervention. lack tailored big-data architectures academics led to several implementation challenges. Purpose article: purpose this article propose architecture enabled big learning analytics. Methods: study reviews existing literature examine tools frameworks; identify An optimized framework real world application was not yet place at global higher educations. Consequently, pipeline experiencing challenges inefficient complex access, slow processing time, security issues associated with transferring system. proposed based on review recent adapts best international practices, guidelines, techniques meet demand current issues. Findings & value added: This identifies implementing projects. Review has been done defined metrics tuned purposes. would increase effectiveness collecting, importing, storing data. Besides, contributes construction full-fledged ecosystem