Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the quantity of online learning interaction content (OLIC) from the perspective of types or frequency, resulting in a limited analysis of the quality of OLIC. Domain concepts as the highest form of interaction are shown as entities or things that are particularly relevant to the educational domain of an online course. The purpose of this paper is to explore a new method to evaluate the quality of OLIC using domain concepts.,This paper proposes a novel approach to automatically evaluate the quality of OLIC regarding relevance, completeness and usefulness. A sample of OLIC corpus is classified and evaluated based on domain concepts and textual features.,Experimental results show that random forest classifiers not only outperform logistic regression and support vector machines but also their performance is improved by considering the quality dimensions of relevance and completeness. In addition, domain concepts contribute to improving the performance of evaluating OLIC.,This paper adopts a limited sample to train the classification models. It has great benefits in monitoring students’ knowledge performance, supporting teachers’ decision-making and even enhancing the efficiency of school management.,This study extends the research of domain concepts in quality evaluation, especially in the online learning domain. It also has great potential for other domains.

中文翻译：

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使用领域概念自动评估在线学习交互内容

互动内容是在在线学习互动过程中创建的，用于交换信息以传达经验和分享知识。先前的研究主要从类型或频率的角度关注在线学习交互内容（OLIC）的数量，导致对OLIC质量的分析有限。作为交互的最高形式的领域概念被显示为与在线课程的教育领域特别相关的实体或事物。本文的目的是探索一种使用领域概念评估 OLIC 质量的新方法。本文提出了一种自动评估 OLIC 质量的相关性、完整性和有用性的新方法。基于领域概念和文本特征对 OLIC 语料库样本进行分类和评估。实验结果表明，随机森林分类器不仅优于逻辑回归和支持向量机，而且通过考虑相关性和完整性的质量维度提高了它们的性能。此外,领域概念有助于提高评估OLIC的性能。,本文采用有限样本来训练分类模型。它在监测学生的知识表现、支持教师的决策甚至提高学校管理效率方面有很大的好处。,本研究扩展了质量评价领域概念的研究,特别是在线学习领域。它还具有用于其他领域的巨大潜力。