Adapting New Learners and New Resources to Micro Open Learning via Online Computation

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 heuristic rules to form the initial recommendation list. By adopting the same set of rules, newly released micro OERs are inserted into established learning paths to increase their discoverability.

Automated summary

Since the outbreak of COVID-19, there has been a high demand for alternative methods of remote learning to keep students on track and prevent them from being exposed to the risk of infection. Education providers have been experimenting with delivering knowledge and learning materials remotely, combining learning management systems, open educational resources, mini applications in social media, and video-conference software to create multi-channel delivery modes. However, the lack of learner information and the continuous release of new resources have posed challenges in implementing innovative and adaptive micro learning. To address the data sparsity issue, an online computation method has been proposed, along with a lightweight learner-micro-OER profile and two algorithmic solutions to tackle the cold start problem for new users and new items.