Journal of e-Learning and Knowledge Society
personal learning system; big data; learning analytics; learning models; e-learning; learning recommendation systems; Information Systems
Business | Educational Administration and Supervision | Educational Assessment, Evaluation, and Research | Educational Leadership | Higher Education | Higher Education Administration | Instructional Media Design | Management Information Systems | Online and Distance Education
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.
Arndt, T. & Guercio, A. (2016). A formalism for PLAN - a big data personal learning assistant for university students. Journal of e-Learning and Knowledge Society, 12(2), retrieved from http://www.je-lks.org/.
This article was published in the Journal of e-Learning and Knowledge Society
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