Knowledge-Aware Learning Analytics for Smart Learning
Author(s) -
Weiqin Chen
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.09.368
Subject(s) - learning analytics , computer science , analytics , data science , domain (mathematical analysis) , big data , data analysis , knowledge management , domain knowledge , data mining , mathematical analysis , mathematics
With the increasing development and adoption of digital technologies for education, more data gathered from educational contexts are being analyzed to give actionable insights to stakeholders. As a data-driven approach for better understanding and optimizing learning and the learning environment, learning analytics has the potential to contribute to smart learning. However, current learning analytics lacks knowledge awareness, an important component in smart learning. This paper draws upon research in the domain of smart learning, reflects on current research on methods and processes in learning analytics, and proposes a framework for knowledge-aware learning analytics for smart learning.
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