Adapting New Learners and New Resources to Micro Open Learning via Online Computation
Bibliographic Data
| ID | 22108917 |
|---|---|
| Authors | Geng Sun (0000-0001-6943-6580, University of Wollongong), Wei Wei (0000-0003-1852-2811, Xi'an University of Technology), Tingru Cui (0000-0002-7899-1372, The University of Melbourne), Dongming Xu (0000-0001-7626-7148, The University of Queensland), Shiping Chen (0000-0002-1934-2372, Commonwealth Scientific and Industrial Research Organisation), Alex Shvonski (Massachusetts Institute of Technology), Li Li (0000-0001-9700-9682, Southwest University), Jun Shen (0000-0002-5769-1223, University of Wollongong), Soheila Garshasbi (University of Wollongong) |
| Year | 2022 |
| Volume | 9 |
| Issue | 6 |
| Pages | 1807-1819 |
| Publication date | 2022-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2022.3210406 |
| OpenAlex | W4311466185 |
| Language | EN |
| Citations received | 2 |
| References cited | 46 |
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
Discoverability · Multimedia · Open Educational Resources · Social media · World Wide Web · Computer Science · Online Learning and Analytics · Open Education and E-Learning · Recommender Systems and Techniques
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |