Big Data Reference Architecture for e-Learning Analytical Systems

Abstract

The recent advancements in technology have produced big data and become the necessity for researcher to analyze the data in order to make it meaningful. Massive amounts of data are collected across social media sites, mobile communications, business environments and institutions. In order to efficiently analyze this large quantity of raw data, the concept of big data was introduced. In this regard, big data analytic is needed in order to provide techniques to analyze the data. This new concept is expected to help education in the near future, by changing the way we approach the e-Learning process, by encouraging the interaction between learners and teachers, by allowing the fulfilment of the individual requirements and goals of learners. The learning environment generates massive knowledge by means of the various services provided in massive open online courses. Such knowledge is produced via learning actor interactions. Also, data analytics can be a valuable tool to help e-Learning organizations deliver better services to the public. It can provide important insights into consumer behavior and better predict demand for goods and services, thereby allowing for better resource management. This result motivates to put forward solutions for big data usage to the educational field. This research article unfolds a big data reference architecture for e-Learning analytical systems to make a unified analysis of the massive data generated by learning actors. This reference architecture makes the process of the massive data produced in big data e-learning system. Finally, the BiDRA for e-Learning analytical systems was evaluated based on the quality of maintainability, modularity, reusability, performance, and scalability.