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Adapting New Learners and New Resources to Micro Open Learning via Online Computation by Geng Sun; Wei Wei; Tingru Cui; Dongming Xu; Shiping Chen; Alex Shvonski; Li Li; Jun Shen; Soheila Garshasbi is a scholarly article available to read on EtoBox.

What is Adapting New Learners and New Resources to Micro Open Learning via Online Computation about?

Since the outbreak of COVID-19, an alternative way to keep students on the track, meanwhile, prevent them from being at the risk of infection is in highly demand. Many education providers had made a move in trial of delivering knowledge and learning materials remotely. Along with this trend, learning management systems, open educational resources (OERs) and OER platforms, mini applications in social media and video-conference software were combined in a rush to create a multi-channel delivery mode to make learning resources openly available round-the-clock. Learning activities in this fast migration to online were regularly found to be carried out in gradual and fragmented time spans. Due to the little-known learner information along with the continuously released new OERs, the cold start problem still hinders the innovative mode of delivery and adaptive micro learning. To overcome the data sparsity, an online computation is proposed to benefit OER providers and instructors. A lightweight learner-micro-OER profile and two algorithmic solutions are provided to tackle the new user and new item cold start problem, respectively. Learning paths are generated and optimized in terms of he

Author

Geng Sun; Wei Wei; Tingru Cui; Dongming Xu; Shiping Chen; Alex Shvonski; Li Li; Jun Shen; Soheila Garshasbi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN