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Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning

Mangaroska, Katerina; Giannakos, Michail
Journal article, Peer reviewed
Accepted version
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URI
http://hdl.handle.net/11250/2582773
Date
2018
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  • Institutt for datateknologi og informatikk [3872]
  • Publikasjoner fra CRIStin - NTNU [20914]
Original version
10.1109/TLT.2018.2868673
Abstract
As the fields of learning analytics and learning design mature, the convergence and synergies between these two fields became an important area for research. This paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. Moreover, this paper presents an overview of what and how learning analytics have been used to inform learning design decisions and in what contexts. The search was performed in seven academic databases, resulting in 43 papers included in the main analysis. The results from the review depict the ongoing design patterns and learning phenomena that emerged from the synergy that learning analytics and learning design impose on the current status of learning technologies. Finally, this review stresses that future research should consider developing a framework on how to capture and systematize learning design data grounded in learning analytics and learning theory, and document what learning design choices made by educators influence subsequent learning activities and performances over time.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal
IEEE Transactions on Learning Technologies

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