2020/05/21 by Mohammadreza Tavakoli, Mirette Elias, Tavakoli, Mohammadreza +5 · 1 citation
Computer Science · #Computers and Society (cs.CY) #Educational Technology and Assessment #FOS: Computer and information sciences #Online Learning and Analytics #Open Education and E-Learning #cs.CY
paper · pdf · doi:10.48550/arxiv.2005.10542
This paper has been accepted to be published in the proceedings of International Conference on Advanced Learning Technologies (ICALT) 2020 by IEEE Computer Society as a short paper
openalex publication_date 2020/05/21 · arxiv created 2020/05/29 · arxiv updated 2020/06/01 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%.