A formalism for PLAN – a big data personal learning assistant for university students

Timothy Arndt, Angela Guercio

Abstract


Big Data-based methods of learning analytics are increasingly relied on by institutions of higher learning in order to increase student retention by identifying at risk students who are in need of an intervention to allow them to continue on in their educational endeavors. It is well known that e-Learning students are even more at risk of failing out of university than are traditional students, so Big Data learning analytics are even more appropriate in this context. In this paper, we present our approach to this problem. We wish to place control of a student’s learning process in his own hands, rather than that of the learning institution in order to decouple the student from the institution since the goals and motivations of these two may not be completely aligned. In this way, we empower the student by giving him control of the personal learning system which employs Big Data techniques to generate recommendations on how to reach a set of learner-specific learning goals. We present the formalism which underlies our system, the architecture which implements the system, scenarios for system use, a survey of related works and thoughts on how the system will be implemented in a prototype in the future.

Keywords


personal learning system, big data, learning recommendation systems, learning models, e-learning

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DOI: https://doi.org/10.20368/1971-8829/1145



Journal of e-Learning and Knowledge Society | ISSN (online) 1971 - 8829 | ISSN (paper) 1826 - 6223 © 2017 Je-LKS - Italian e-Learning Association (SIe-L).