Classifying discourse in a CSCL platform to evaluate correlations with Teacher Participation and Progress
This work addresses the challenge for teachers in CSCL environments by providing automated tools to assess student engagement and progress, though it appears incremental in applying existing classification methods to this domain.
The study tackled the problem of monitoring and engaging learners in computer-supported collaborative learning by analyzing natural language interactions to produce qualitative indicators for teachers, finding that certain discourse types correlate with learner progress and teacher emotive participation.
In Computer-Supported learning, monitoring and engaging a group of learners is a complex task for teachers, especially when learners are working collaboratively: Are my students motivated? What kind of progress are they making? Should I intervene? Is my communication and the didactic design adapted to my students? Our hypothesis is that the analysis of natural language interactions between students, and between students and teachers, provide very valuable information and could be used to produce qualitative indicators to help teachers' decisions. We develop an automatic approach in three steps (1) to explore the discursive functions of messages in a CSCL platform, (2) to classify the messages automatically and (3) to evaluate correlations between discursive attitudes and other variables linked to the learning activity. Results tend to show that some types of discourse are correlated with a notion of Progress on the learning activities and the importance of emotive participation from the Teacher.