Année : 2018
Lieu de publication de l'article :

Résumé de l'article

Learning analytics are used to track learners’ progress andempower educators and learners to make well-informed data-driven decisions. However, due to the distributed nature ofthe learning process, analytics need to be combined to of-fer broader insights into learner’s behavior and experiences.Consequently, this paper presents an architecture of a learn-ing ecosystem, that integrates and utilizes cross-platformanalytics. The proposed cross-platform architecture has beenput into practice via a Java programming course. After a se-ries of studies, a proof of concept was derived that showshow cross-platform analytics amplify the relevant analyticsfor the learning process. Such analytics could improve edu-cators’ and learners’ understanding of their own actions andthe environments in which learning occurs.CCS CONCEPTS• Applied computing → Interactive learning environ-ments;KEYWORDSlearning analytics, multimodal systems, architectureACM Reference Format:, Katerina Mangaroska, Boban Vesin, and Michail Giannakos. 2019.Cross-PlatformAnalytics: A step towards Personalization andAdap-tation in Education. In The 9th International Learning Analytics &Knowledge Conference (LAK19), March 4–8, 2019, Tempe, AZ, USA.ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3303772.3303825Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bearthis notice and the full citation on the first page. Copyrights for componentsof this work owned by others than ACMmust be honored. Abstracting withcredit is permitted. To copy otherwise, or republish, to post on servers or toredistribute to lists, requires prior specific permission and/or a fee. Requestpermissions from permissions@acm.org.LAK19, March 4–8, 2019, Tempe, AZ, USA© 2019 Association for Computing Machinery.ACM ISBN 978-1-4503-6256-6/19/03. . . $15.00https://doi.org/10.1145/3303772.33038251

Mots-clés

Machine learning,Drag and drop,