Is it a good move? Mining effective tutoring strategies from human-human tutorial dialogues
Number of pages
SourceFuture Generation Computer Systems, 127, (2022), pp. 194-207
Article / Letter to editor
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SW OZ BSI OLO
Future Generation Computer Systems
SubjectLearning and Plasticity
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).
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