Detecting Off-Task Behavior from Student Dialogue in Game-Based Collaborative Learning
Detecting Off-Task Behavior from Student Dialogue in Game-Based Collaborative Learning
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DOI：
10.1007/978-3-030-52237-7_5
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发表时间：
2020-06-09
期刊：
影响因子：
--
通讯作者：
Lester JC
中科院分区：
文献类型：
--
作者：
Carpenter D;Emerson A;Mott BW;Saleh A;Glazewski KD;Hmelo-Silver CE;Lester JC
基于游戏的协作学习环境集成了基于游戏的学习和协作学习。这些环境为学生提供了共同的目标，并为他们提供了沟通的手段，使他们能够共享信息、提出问题、构建解释并共同努力实现共同的目标。协作学习的一个关键挑战是学生可能会进行非生产性的对话，这可能会影响学习活动和结果。基于游戏的协作学习环境可以实时检测这种偏离任务的行为，有可能通过将对话重新引导回更有成效的主题来增强学生之间的协作。本文研究了如何使用对话分析将学生的对话话语分类为任务外或任务中。我们利用从 13 个小组（每组 4 名学生）收集的课堂数据，训练了来自集成到 Crystal Island: EcoJourneys（一个针对中学生态系统科学的基于游戏的协作学习环境）中的群聊功能的文本消息的任务外对话模型。我们评估了使用不同词嵌入（即 word2vec、ELMo 和 BERT）的任务外对话模型以及从聊天日志中捕获不同数量的上下文信息的预测性任务外对话模型的有效性。结果表明，与不利用此信息的模型相比，包含最近上下文窗口并表示聊天消息的顺序性质的预测性脱任务对话模型可实现更高的预测性能。这些发现表明，基于协作游戏的学习环境的脱任务对话模型可以可靠地识别和预测学生的脱任务行为，这为自适应地构建协作对话提供了机会。
Collaborative game-based learning environments integrate game-based learning and collaborative learning. These environments present students with a shared objective and provide them with a means to communicate, which allows them to share information, ask questions, construct explanations, and work together toward their shared goal. A key challenge in collaborative learning is that students may engage in unproductive discourse, which may affect learning activities and outcomes. Collaborative game-based learning environments that can detect this off-task behavior in real-time have the potential to enhance collaboration between students by redirecting the conversation back to more productive topics. This paper investigates the use of dialogue analysis to classify student conversational utterances as either off-task or on-task. Using classroom data collected from 13 groups of four students, we trained off-task dialogue models for text messages from a group chat feature integrated into Crystal Island: EcoJourneys, a collaborative game-based learning environment for middle school ecosystem science. We evaluate the effectiveness of the off-task dialogue models, which use different word embeddings (i.e., word2vec, ELMo, and BERT), as well as predictive off-task dialogue models that capture varying amounts of contextual information from the chat log. Results indicate that predictive off-task dialogue models that incorporate a window of recent context and represent the sequential nature of the chat messages achieve higher predictive performance compared to models that do not leverage this information. These findings suggest that off-task dialogue models for collaborative game-based learning environments can reliably recognize and predict students’ off-task behavior, which introduces the opportunity to adaptively scaffold collaborative dialogue.
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DOI：
10.1016/j.ijhcs.2009.12.003
发表时间：
2010-04-01
影响因子：
5.4
作者：
Baker, Ryan S. J. D.;D'Mello, Sidney K.;Graesser, Arthur C.
通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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影响因子：
4.8
作者：
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DOI：
10.1007/s40593-013-0012-6
发表时间：
2014-01-01
影响因子：
4.9
作者：
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通讯作者：
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