Computer Monitored Problem Solving Dialogues
Computer Monitored Problem Solving Dialogues
复制标题
计算机监控的问题解决对话
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Nicole Rutt
中科院分区：
文献类型：
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作者：
Lisa Dion;Jeremy Jank;Nicole Rutt
这个项目“看在肩上”在学生合作参与数学问题解决活动。我们研究的一个任务是机械地对学生当前的活动或知识状态进行分类，我们已经确定了15个不同的类别。我们已经制作了一个自动分类器，可以检查学生的句子，并且在识别包含某些知识或某些活动证据的话语时准确率为55%。该分类器是建立在一个语料库的学生写的报告。将每个句子视为一个词袋，使用非负矩阵分解（NMF）和潜在语义分析（LSA）建立词共现矩阵的向量空间模型。分类是通过比较新的，未知的，句子与预先建立的捆绑手动标记的句子，一个捆绑为每个分类。我们的分类是针对要解决的问题的，是理解一个名为毒药的双人游戏所需的特定知识。我们也一直在描述与问题无关的类别的对话：数学合作的维度和解决问题的维度。这将使我们能够对学生参与对话和解决问题过程的方式进行分类。这项工作的背景是一个定量的解决问题的过程中，学生在小组工作。我们的目标是让计算机注意到教师在教室里走动时可能观察到的活动的一些相同方面，例如一个小组实现了什么，学生如何合作。这种以计算机为媒介的协作解决问题的方式暴露了学生的思维，提供了获得学生学习见解的机会。
This project “looks over the shoulder” at students collaboratively engaged in a math problem-solving activity. One task we looked at was mechanically classifying the students current activity or knowledge state, of which we have identified 15 different categories. We have produced an automatic classifier that examines student sentences and is 55% accurate in identifying utterances as containing certain bits of knowledge or evidence of certain activities. The classifier was built from a corpus of student-written reports. Treating each sentence as a bag of words, we built vector space models of the word co-occurrence matrix using both non-negative matrix factorization (NMF) and latent semantic analysis (LSA). Classification was achieved by comparing new, unknown, sentences with pre-built bundles of manually tagged sentences, one bundle for each classification. Our categories are specific to the problem being solved, particular bits of knowledge needed to understand a two-person game called Poison. We have also been characterizing the dialogues with problem-independent categories: a math collaborative dimension and a problem-solving dimension. This will enable us to classify utterances with regard to in what ways students are participating in the dialogue and the problem-solving process. The context of this work is a quantitative problem-solving course in which students work in small groups. Our goal is for the computer to notice some of the same aspects of the activity that a teacher walking around the classroom might observe, such as what realizations a group has achieved and how students are collaborating. This type of computer-mediated collaborative problem solving exposes student thinking, providing opportunities to gain insights about student learning.