Dynamic Bayesian Network Models for Peer Tutoring Interactions
Dynamic Bayesian Network Models for Peer Tutoring Interactions
复制标题
用于同伴辅导互动的动态贝叶斯网络模型
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
A. Ogan
中科院分区：
文献类型：
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作者：
Yoav Bergner;Erin Walker;A. Ogan
在协作学习中自动区分有效和无效模式的能力为成对或小组学习提供了更好的机会，即使老师可能无法提供便利。在本章中，使用隐马尔可夫模型（hmm）以两种方式对一次性基于计算机的同伴辅导课程的数据进行建模。第一个模型使用输入输出HMM来比较不同导师输入在帮助导师纠正解决方案中错误步骤的帮助价值。该模型仅使用基于上下文和导师聊天认知内容的自动生成代码。第二个模型预测了实验条件下从前测到后测的归一化增益。包括导师聊天的认知和情感标签（人类编码），以及导师的正确性、撤销和回复导师的聊天。HMM的性能优于“静态”逻辑回归模型，该模型使用相同可观测值的聚合总数。一些隐藏状态很容易解释，尽管高增益组和低增益组之间更深入的比较是正在进行的工作的一部分。
The ability to automatically distinguish between effective and ineffective patterns in collaborative learning sessions opens doors to improved opportunity for learning in pairs or groups even when a teacher might not be available to facilitate. In this chapter, data from one-time computer-based peer tutoring sessions are modeled using hidden Markov models (HMMs) in two ways. The first model uses an input–output HMM to compare the assistance value of different tutor inputs in helping the tutee correct a mistaken step in solution. This model uses only automatically generated codes based on context and cognitive content of the tutor chat. The second model predicts tutee normalized gains from pre- to posttest in the experimental condition. Both cognitive and affective labels to tutor chats (human coded) were included as well as tutee (in)correctness, undos, and chats back to the tutor. Performance of the HMM is favorable compared to a “static” logistic regression model using aggregated totals of the same observables. Some of the hidden states are readily interpretable, though deeper comparison between high- and low-gain groups is part of ongoing work.