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Educational Data Mining and Learning Analytics for Improving Online Learning Environments

2020; Arizona State University; Paredes, Y.; Siegle, R.; Hsiao, I.; Craig, S.

The proliferation of educational technology systems has led to the advent of a large number of datasets related to learner interaction. New fields have emerged which aim to use this data to identify interventions that could help the learners become efficient and effective in their learning. However, these systems have to follow user-centered design principles to ensure that the system is usable and the data is of high quality. Human factors literature is limited on the topics regarding Educational Data Mining (EDM) and Learning Analytics (LA). To develop improved educational systems, it is important for human factors engineers to be exposed to these data-oriented fields. This paper aims to provide a brief introduction to the fields of EDM and LA, discuss data visualization and dashboards that are used to convey results to learners, and finally to identify where human factors can aid other fields.

Contract: HQ0034-19-C-0015

DTIC: AD1124949

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