To construct dialogue-based Intelligent Tutoring Systems (ITS) with sufficient pedagogical expertise, a trendy research method is to mine large-scale data collected by existing dialogue-based ITS or generated between human tutors and students to discover effective tutoring strategies. However, most of the existing research has mainly focused on the analysis of successful tutorial dialogue. We argue that, to better inform the design of dialogue-based ITS, it is also important to analyse unsuccessful tutorial dialogues and gain a better understanding of the reasons behind those failures. Therefore, our study aimed to identify effective tutoring strategies by mining a large-scale dataset of both successful and unsuccessful human–human online tutorial dialogues, and further used these tutoring strategies for predicting students’ problem-solving performance. Specifically, the study adopted a widely-used educational dialogue act scheme to describe the action behind utterances made by a tutor/student in the broader context of a tutorial dialogue (e.g., asking/answering a question, providing hints). Frequent dialogue acts were identified and analysed by taking into account the prior progress that a student had made before the start of a tutorial session and the problem-solving performance the student achieved after the end of the session. Besides, we performed a sequence analysis on the inferred actions to identify prominent patterns that were closely related to students’ problem-solving performance. These prominent patterns could shed light on the frequent strategies used by tutors. Lastly, we measured the power of these tutorial actions in predicting students’ problem-solving performance by applying a well-established machine learning method, Gradient Tree Boosting (GTB). Through extensive analysis and evaluations, we identified a set of different action patterns that were pertinent to tutors and students across dialogues of different traits, e.g., students without prior progress in solving problems, compared to those with prior progress, were likely to receive more thought-provoking questions from their tutors. More importantly, we demonstrated that the actions taken by students and tutors during a tutorial process could not adequately predict student performance and should be considered together with other relevant factors (e.g., the informativeness of the utterances).

为了构建具有足够教学专业知识的基于对话的智能辅导系统（ITS），一种流行的研究方法是挖掘现有的基于对话的 ITS 收集的或人类导师与学生之间产生的大规模数据，以发现有效的辅导策略。然而，现有的大多数研究主要集中在对成功教程对话的分析上。我们认为，为了更好地为基于对话的 ITS 设计提供信息，分析不成功的教程对话并更好地了解这些失败背后的原因也很重要。因此，我们的研究旨在通过挖掘成功和不成功的人与人在线教程对话的大规模数据集来确定有效的辅导策略，并进一步使用这些辅导策略来预测学生解决问题的表现。具体而言，该研究采用了广泛使用的教育对话行为方案来描述导师/学生在更广泛的教程对话背景下（例如，提出/回答问题，提供提示）的话语背后的行为。通过考虑学生在辅导课程开始之前取得的先前进展以及学生在课程结束后取得的解决问题的表现，识别和分析了频繁的对话行为。此外，我们对推断的动作进行了序列分析，以确定与学生解决问题的表现密切相关的突出模式。这些突出的模式可以阐明导师经常使用的策略。最后，我们通过应用成熟的机器学习方法梯度树提升 (GTB) 来衡量这些教程操作在预测学生解决问题的能力方面的能力。通过广泛的分析和评估，我们确定了一套与导师和学生相关的不同行为模式，这些行为模式在不同特征的对话中与导师和学生相关，例如，与之前有进步的学生相比，在解决问题方面没有进步的学生可能会得到更多的思考- 从他们的导师那里挑起问题。更重要的是，我们证明了学生和导师在辅导过程中采取的行动不能充分预测学生的表现，应该与其他相关因素（例如话语的信息量）一起考虑。