Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

By Katerina Mangaroska, Michail N. Giannakos. IEEE Transactions on Learning Technologies, 2019

302 citations recorded by the source corpus.

As the fields of learning analytics and learning design mature, the convergence and synergies between the two are becoming an important area for research. This paper intends to summarize the main outcomes of a systematic 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.

Subfields: Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/4f14a3208472d47da7f5aabc5b7abf183494e9cb

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