Educational organizations are operating in a dynamic competitive environment. Online learning, education, emerging technologies, and the number of learners are growing very fast and produce big data that contain meaningful information to be extracted and processed. The idea of applying the big data in e-learning supports developing and analyzing the students as well as the educational organization’s stakeholders. A big data framework architecture, methods, and tools in the e-learning platform are introduced. The paper studies briefly the opportunities of big data deployment in the educational sector and the value that the students and organizations gain. However, it also highlights the challenges and future research directions as the tool’s selection and value extraction from complex educational data sets.
With the undergoing technological revolution in education, adapting recommender systems to the personalized elearning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.
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