Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment

Technology enhanced learning (TEL) such as online learning environment with adaptive technologies has gained growing interest in recent past in the field of teaching and learning. In this context, mobile learning has got much momentum and is exemplified by diverse characteristics associated with the technologies and devices used, the enormous size of data generated throughout a learning session, and the interactions among the learners that occur outside the classroom. Consequently, sophisticated data analysis techniques are required to handle the intricacy of mobile learning and analyze the vast amount of datasets to enhance the learning experiences of mobile learners. This has led to the adoption of big data analytics for efficient processing of big learning data to add value to the mobile learning environments. Yet limited processing capability of the mobile devices is another key challenge faced by such big data analytics in mobile learning environments. To overcome this limitation, certain heavy computational parts could be offloaded to the cloud which can provide enough computation and storage resources. To this end, this paper presents a cloud based mobile learning framework that utilizes big data analytics technique to extract values from huge volume of mobile learners' data. Finally, we investigate learners' readiness and driving factors of mobile learning adoption in higher education institutions. In particular, we propose a hypothesized model for mobile learning adoption built on a locally extended technology acceptance model (TAM).