Towards domain general detection of transactive knowledge building behavior

James Fiacco, C. Rosé
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引用次数: 3

Abstract

Support of discussion based learning at scale benefits from automated analysis of discussion for enabling effective assignment of students to project teams, for triggering dynamic support of group learning processes, and for assessment of those learning processes. A major limitation of much past work in machine learning applied to automated analysis of discussion is the failure of the models to generalize to data outside of the parameters of the context in which the training data was collected. This limitation means that a separate training effort must be undertaken for each domain in which the models will be used. This paper focuses on a specific construct of discussion based learning referred to as Transactivity and provides a novel machine learning approach with performance that exceeds state-of-the-art performance within the same domain in which it was trained and a new domain, and does not suffer any reduction in performance when transferring to the new domain. These results stand as an advance over past work on automated detection of Transactivity and increase the value of trained models for supporting group learning at scale. Implications for practice in at-scale learning environments are discussed.
面向交互知识构建行为的领域通用检测
基于讨论的大规模学习的支持受益于讨论的自动分析,从而能够有效地将学生分配给项目团队,触发对小组学习过程的动态支持,以及对这些学习过程的评估。过去许多机器学习应用于讨论的自动分析的主要限制是,模型无法推广到收集训练数据的上下文参数之外的数据。这一限制意味着必须为使用模型的每个领域进行单独的训练工作。本文关注的是一种基于讨论的学习的特定结构,称为Transactivity,并提供了一种新的机器学习方法,其性能在其训练的同一领域和新领域内超过了最先进的性能,并且在转移到新领域时不会受到性能降低的影响。这些结果是对过去自动检测交互性工作的一种进步,并增加了训练模型的价值,以支持大规模的群体学习。讨论了在大规模学习环境中实践的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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