Towards automatic cross-language classification of cognitive presence in online discussions

Gian Barbosa, Raissa Camelo, Anderson Pinheiro Cavalcanti, P. Miranda, R. F. Mello, Vitomir Kovanovíc, D. Gašević
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引用次数: 28

Abstract

This paper presents a study that examined automated cross-language classification of online discussion messages for the levels of cognitive presence, a key construct from the widely used Community of Inquiry (CoI) model of online learning. Specifically, we examined the classification of 1,500 Portuguese language discussion messages using a classifier trained on a corpus of the 1,747 English language discussion messages. In the study, a random forest classifier was developed using a small set of 108 validated indicators of psychological processes, linguistic coherence, and online discussion structure. The classifier obtained 67% accuracy and Cohen's κ of 0.32, showing a moderate level of inter-rater agreement above chance and the general viability of the proposed approach. Most importantly, the findings suggest that certain aspects of cognitive presence construct are highly generalizable and transfer across different languages. Finally, the paper also presents a novel method for addressing class imbalance problem using a generic algorithm heuristic technique, which provided substantial improvements over the use of imbalanced dataset. Results and practical implications are further discussed.
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网络讨论中认知存在的自动跨语言分类研究
本文提出了一项研究,该研究检查了在线讨论消息的认知存在水平的自动跨语言分类,这是广泛使用的在线学习社区(CoI)模型的关键结构。具体来说,我们使用在1,747个英语讨论消息的语料库上训练的分类器检查了1,500个葡萄牙语讨论消息的分类。在这项研究中,随机森林分类器使用了108个经过验证的心理过程、语言连贯性和在线讨论结构指标。该分类器获得了67%的准确率和0.32的Cohen’s κ,显示了中等水平的评分间一致性高于概率和所提出方法的总体可行性。最重要的是,研究结果表明,认知在场结构的某些方面是高度概括的,并在不同的语言中转移。最后,本文还提出了一种使用通用算法启发式技术来解决类不平衡问题的新方法,该方法在使用不平衡数据集的基础上进行了实质性的改进。进一步讨论了结果和实际意义。
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