Characterizing Interactive Communications in Computer-Supported Collaborative Problem-Solving Tasks: A Conditional Transition Profile Approach

Communication in a collaborative problem-solving activity plays a pivotal role in the success of the collaboration in both academia and the workplace. Computer-supported collaboration makes it possible to collect large-scale communication data to investigate the process at a finer granularity. In this paper, we introduce a conditional transition profile (CTP) to characterize aspects of each team member's communication. Based on the data from a large-scale empirical study, we found that participants in the same team tend to show similar CTP compared to participants from different teams. We also found that team members who showed more “negotiation” after the partner “shared” information tended to show more improvement after the collaboration while those who continued sharing ideas while their partners were negotiating tended to improve less

Automated labelling of dialogue modes in tutorial dialogues

We present in this paper a study whose goal was to automatically label higher level constructs, called dialogue modes, in tutorial dialogues. Each tutorial dialogue is regarded as a sequence of utterances articulated by either the learner or the tutor. The dialogue utterances can be grouped into dialogue modes which correspond to general conversational phases such as dialogue openings, e.g. when the conversational partners greet each other, or serve specific pedagogical purposes, e.g. a scaffolding students\u27 problem solving process. Detecting dialogue modes is important because they can be used as an instrument to understand what good tutors do at a higher level of abstraction, thus, enabling more general conclusions about good tutoring. We propose an approach to the dialogue mode labeling problem based on Conditional Random Fields, a powerful machine learning technique for sequence labeling which has net advantages over alternatives such as Hidden Markov Models. The downside of the Condition Random Fields approach is that it requires annotated data while the Hidden Markov Models approach is unsupervised. The performance of the approach on a large data set of 1,438 tutoring sessions yielded very good results compared to human generated tags