Considering Alternate Futures to Classify Off-Task Behavior as Emotion Self-Regulation: A Supervised Learning Approach
Considering Alternate Futures to Classify Off-Task Behavior as Emotion Self-Regulation: A Supervised Learning Approach
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DOI：
10.5281/zenodo.3554607
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发表时间：
2013-05
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影响因子：
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通讯作者：
Jennifer Sabourin;Jonathan P. Rowe;Bradford W. Mott;James C. Lester
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作者：
Jennifer Sabourin;Jonathan P. Rowe;Bradford W. Mott;James C. Lester
在过去的十年里，人们对实时评估学生在与教育软件交互过程中的参与度和动机越来越感兴趣。检测脱离的症状，如脱离任务行为，对于理解学生在学习过程中的动机特征显示出相当大的希望。本研究通过分析学生与“水晶岛”（CRYSTAL ISLAND）的互动数据，探讨了任务外行为在中学微生物学教学中的情感作用。我们观察到，任务外行为与学生学习能力下降有关，但对学生情感转变的初步分析表明，任务外行为也可能在一些学生应对消极情感状态（如挫折）方面发挥积极作用。实证研究结果表明，一些学生在学习过程中可能会使用任务外行为作为自我调节消极情绪状态的策略。基于这些观察，我们引入了一个监督机器学习程序来检测学生的非任务行为是否是情绪自我调节的情况。该方法分三个阶段进行。在第一阶段，训练一个动态贝叶斯网络（DBN），利用从与学习环境的互动中收集的数据来模拟学生情绪自我报告的效价。在第二阶段，一个新颖的模拟过程使用DBN通过模拟学生的情感轨迹来产生不同的未来，就好像他们在实际学习互动中参与的任务外行为比他们在实际学习互动中参与的行为要少。将备选未来与学生的实际轨迹进行比较，以产生标签，表示学生的非任务行为是否属于情绪自我调节的情况。在最后阶段，使用现成的分类器和可以在运行时设置中计算的特征来预测生成的情绪自我调节标签。结果表明，这种方法有望识别出属于情绪自我调节的任务外行为。前两个阶段的分析表明，经过训练的DBN模型能够准确地模拟CRYSTAL ISLAND学生的任务外行为与自我报告的情绪效价之间的关系。此外，所提出的模拟过程产生的情绪自我调节标签具有高水平的可靠性。初步分析表明，支持向量机、袋装树和随机森林在预测生成的情绪自我调节标签方面表现出希望，但仍有改进的空间。研究结果强调了在以叙事为中心的学习环境中，在模拟学生情绪自我调节过程时考虑不同未来的方法潜力。
Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as offtask behavior, has shown considerable promise for understanding students’ motivational characteristics during learning. In this paper, we investigate the affective role of off-task behavior by analyzing data from student interactions with CRYSTAL ISLAND , a narrative-centered learning environment for middle school microbiology. We observe that off-task behavior is associated with reduced student learning, but preliminary analyses of students’ affective transitions suggest that off-task behavior may also serve a productive role for some students coping with negative affective states such as frustration. Empirical findings imply that some students may use off-task behavior as a strategy for self-regulating negative emotional states during learning. Based on these observations, we introduce a supervised machine learning procedure for detecting whether students’ off-task behaviors are cases of emotion self-regulation. The method proceeds in three stages. During the first stage, a dynamic Bayesian network (DBN) is trained to model the valence of students’ emotion selfreports using collected data from interactions with the learning environment. In the second stage, a novel simulation process uses the DBN to generate alternate futures by modeling students’ affective trajectories as if they had engaged in fewer off-task behaviors than they did during their actual learning interactions. The alternate futures are compared to students’ actual traces to produce labels denoting whether students’ off-task behaviors are cases of emotion self-regulation. In the final stage, the generated emotion self-regulation labels are predicted using off-the-shelf classifiers and features that can be computed in run-time settings. Results suggest that this approach shows promise for identifying cases of off-task behavior that are emotion selfregulation. Analyses of the first two phases suggest that trained DBN models are capable of accurately modeling relationships between students’ off-task behaviors and self-reported emotional valence in CRYSTAL ISLAND . Additionally, the proposed simulation process produces emotion self-regulation labels with high levels of reliability. Preliminary analyses indicate that support vector machines, bagged trees, and random forests show promise for predicting the generated emotion self-regulation labels, but room for improvement remains. The findings underscore the methodological potential of considering alternate futures when modeling students’ emotion self-regulation processes in narrative-centered learning environments.