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Title: Learning Analytics Infrastructure for Seamless Learning
Authors: Flanagan, Brendan
Ogata, Hiroaki  kyouindb  KAKEN_id
Author's alias: 緒方, 広明
Keywords: Seamless learning
formal/informal learning analytics
Issue Date: 2018
Publisher: Association for Computing Machinery (ACM)
Journal title: Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18)
Abstract: Seamless learning offers the opportunity to learn in different environments regardless of location or time. It can also provide insights for teachers into how learning is being conducted in informal situations outside the classroom. Previous work into the analysis of seamless learning has mainly focused on purpose build specialized systems that provide an environment for a specific task. However, as the field of learning analytics matures, we are increasingly seeing the development of modular systems that can be linked together by standards based protocols. This paper proposes the integration of the SCROLL system into a wider modular system to increase the possibilities of seamless learning analytics to inform blended learning design. The proposed system addresses fundamental problems, such as the protection of user privacy and authentication while increasing the availability of data for analysis from other learning systems. Data is collected and stored centrally in a unified form that provides the ability to analyze and visualize learning across numerous environments and contexts.
Description: LAK’18: 8th International Learning Analytics and Knowledge (LAK) Conference, SMC Conference & Function Centre in Sydney, Australia on March 5–9, 2018.
Rights: This work is published under the terms of the Creative Commons Attribution- Noncommercial-ShareAlike 3.0 Australia Licence. Under this Licence you are free to: Share - copy and redistribute the material in any medium or format.
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