论文标题
开放教育资源的质量预测基于元数据的方法
Quality Prediction of Open Educational Resources A Metadata-based Approach
论文作者
论文摘要
在最近的十年中，在线学习环境积累了数百万个开放教育资源（OER）。但是，对于学习者而言，找到相关和高质量的OER是一项复杂且耗时的活动。此外，元数据在提供建议和搜索等高质量服务方面起着关键作用。元数据也可以用于自动质量控制，因为鉴于OER的数量不断增加，手动质量控制变得越来越困难。在这项工作中，我们收集了8,887 OER的元数据，以执行探索性数据分析，以观察质量控制对元数据质量的影响。随后，我们提出了一个OER元数据评分模型，并建立一个基于元数据的预测模型以预测OER的质量。根据我们的数据和模型，我们能够检测出F1分数为94.6％的高质量OER。
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%.
