Classifying discourse in a CSCL platform to evaluate correlations with Teacher Participation and Progress

TL;DR

This paper presents an automatic method to classify discourse in a CSCL platform, revealing correlations between message types, teacher participation, and student progress to support better instructional decisions.

Contribution

It introduces a three-step approach for analyzing natural language interactions in CSCL, linking discourse functions to learning progress and teacher engagement.

Findings

Certain discourse types correlate with learning progress.

Teacher emotive participation influences student engagement.

Automated classification aids in monitoring collaborative learning.

Abstract

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.