现实世界议论文中的知识建构与不确定性:文本分析方法

Ha Nguyen, William Young
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引用次数: 1

摘要

协作论证是促进对科学问题理解的关键。然而,课堂结构可能并不总是让学生做好参与辩论的准备。为了应对这一挑战,教育研究者研究了社会知识构建和管理群体理解中的不确定性的重要性。在这项研究中,我们使用来自/r/ChangeMyView的数据来探索这些过程,ChangeMyView是Reddit上的一个在线论坛,用户在这里发表自己的观点,让其他人参与批评想法,并在讨论修改了他们的观点时承认。这种不便利的环境可以说明辩论是如何自然地向精炼的观点发展的。我们使用自动文本分析(LIWC)和话语分析来理解成功论点的特征和话语序列。我们发现,如果争论的线索集中在思想的表达、连贯性和语义多样性上,就更有可能成功。研究结果强调了不确定性的作用:带有更多确定性词汇的线索不太可能成功。此外,成功的论点的特点是提出、管理和减少不确定性的循环,并有更多的证据和观点结合。我们讨论了学习环境如何为思想构建、一致性和不确定性创造规范,以及在检测到非生产性论证序列时提供自适应提示以维持和减少不确定性的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge Construction and Uncertainty in Real World Argumentation: A Text Analysis Approach
Collaborative argumentation is key to promoting understanding of scientific issues. However, classroom structures may not always prepare students to engage in argumentation. To address this challenge, education researchers have examined the importance of social knowledge construction and managing uncertainty in group understanding. In this study, we explore these processes using data from /r/ChangeMyView, an online forum on Reddit where users present their opinions, engage others in critiquing ideas, and acknowledge when the discussion has modified their opinions. This unfacilitated environment can illuminate how argumentation evolves naturally towards refined opinions. We employ automated text analyses (LIWC) and discourse analyses to understand the features and discourse sequences of successful arguments. We find that argumentative threads are more likely to be successful if they focus on idea articulation, coherence, and semantic diversity. Findings highlight the role of uncertainty: threads with more certainty words are less likely to be successful. Furthermore, successful arguments are characterized by cycles of raising, managing, and reducing uncertainty, with more occurrences of evidence and idea incorporation. We discuss how learning environments can create norms for idea construction, coherence, and uncertainty, and the potential to provide adaptive prompts to maintain and reduce uncertainty when unproductive argumentative sequences are detected.
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