Although widely used, the SCORM metadata model for content aggregation is difficult to be used by educators, content developers and instructional designers. Particularly, the identification of contents related with each other, in large repositories, and their aggregation using metadata as defined in SCORM, has been demanding efforts of computer science researchers in pursuit of the automation of this process. Previous approaches have extended or altered the metadata defined by SCORM standard. In this paper, we present experimental results on our proposed methodology which employs ontologies, automatic annotation of metadata, information retrieval and text mining to recommend and aggregate related content, using the relation metadata category as defined by SCORM. We developed a computer system prototype which applies the proposed methodology on a sample of learning objects generating results to evaluate its efficacy. The results demonstrate that the proposed method is feasible and effective to produce the expected results.

中文翻译：

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根据SCORM自动进行内容推荐和汇总

尽管已广泛使用，但用于内容聚合的SCORM元数据模型很难被教育工作者，内容开发人员和教学设计人员使用。尤其是，在大型存储库中彼此相关的内容的标识以及使用SCORM中定义的元数据对其进行汇总，一直是计算机科学研究人员在追求此过程自动化方面的工作。先前的方法已经扩展或更改了SCORM标准定义的元数据。在本文中，我们介绍了我们提出的方法的实验结果，该方法利用本体，元数据的自动注释，信息检索和文本挖掘来推荐和汇总相关内容，并使用SCORM定义的关系元数据类别。我们开发了一种计算机系统原型，该原型将所提出的方法应用于学习对象的样本中，并生成结果以评估其功效。结果表明，该方法是可行和有效的，以产生预期的结果。