Abstract
As the fields of learning analytics and learning design mature, the convergence and synergies between them become an important area for research. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing learning. Hence, this paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. The search was performed in seven academic databases, resulting in 38 papers included in the main analysis. The review demonstrates ongoing design patterns and learning phenomena that improve learning, by providing more comprehensive background of the current landscape of learning analytics for learning design and its impact on the current status of learning technologies. Consequently, future research should consider how to capture and systematize learning design data. Moreover, it should evaluate and document what learning design choices made by educators using what learning analytics techniques influence learning experiences and learning performances over time.
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This work was supported by the Research Council of Norway under the project FUTURE LEARNING (255129/H20).
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Mangaroska, K., Giannakos, M. (2017). Learning Analytics for Learning Design: Towards Evidence-Driven Decisions to Enhance Learning. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds) Data Driven Approaches in Digital Education. EC-TEL 2017. Lecture Notes in Computer Science(), vol 10474. Springer, Cham. https://doi.org/10.1007/978-3-319-66610-5_38
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DOI: https://doi.org/10.1007/978-3-319-66610-5_38
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