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  1. Research Outputs

Learning analytics in MOOCs: EMMA case

Chapter
Publication Date:
2017
Short description:
Learning analytics in MOOCs: EMMA case / Eradze, M., Tammets, K. (STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION). - In: Studies in Classification, Data Analysis, and Knowledge Organization[s.l] : Springer Berlin Heidelberg, 2017. - ISBN 978-3-319-55476-1. - pp. 193-204 [10.1007/978-3-319-55477-8_18]
abstract:
The paper overviews the project—European Multiple MOOC Aggre-gator, EMMA for short, and its learning analytics system with the initial results. xAPI statements are used for designing learning analytics dashboards in order to provide instant feedback for learners and instructors. The paper presents dashboard visualizations and discusses the possibilities of use of EMMA learning analytics dashboard views for sensemaking and reflection of the MOOCs and MOOC experience. It investigates some of the MOOCs in EMMA platform as cases and analyzes the learning designs of those MOOCs. Recommendations of changes to learning designs based on learning analytics data are provided.
Iris type:
Capitolo/Saggio
List of contributors:
Eradze, M.; Tammets, K.
Handle:
https://iris.unimore.it/handle/11380/1221818
Book title:
Studies in Classification, Data Analysis, and Knowledge Organization
Published in:
STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION
Series
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