Abstract
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.
Highlights
Technology advancement allows computer-supported collaboration to be widely adopted in both academia and the workplace
We developed a framework for coding the communication data in collaborative problem solving (CPS) (Liu et al, 2015) based on CSCL literature and the assessment frameworks from PISA 2015 (Organization for Economic Co-operation and Development, 2013) and ATC21S (Griffin et al, 2012)
Given the interdependent nature of dyadic communication, we might expect the conditional transition profile (CTP) between the team members to be more correlated than those between random pairs of participants, which can serve as a check of the plausibility of the CTP approach
Summary
Technology advancement allows computer-supported collaboration to be widely adopted in both academia and the workplace. The communication data in computer-supported collaboration contain rich information regarding the collaboration process. Understanding the communication process will help to identify pathways to more successful collaboration outcomes. Such knowledge can further inform the development of real-time facilitation or intervention mechanisms to scaffold the collaboration. The analysis of communication data (or discourse analysis as it is often called in the computer-supported collaborative learning (CSCL) community) usually starts with the coding or labeling of each turn (or several turns that constitute large speech units) of communications based on a framework (rubrics) being developed to address specific research questions. Based on human-coded discourse, natural language processing (NLP) techniques can Conditional Transition Profile be employed to automate the annotation to an accuracy level that is close to human coding (Rosé et al, 2008; Rus et al, 2015; Flor et al, 2016; Hao et al, 2017a)
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