Automated Prediction of Student Participation in Collaborative Dialogs Using Time Series Analyses

In: info:eu-repo/grantAgreement/EC/H2020/644187/EU/Realising an Applied Gaming Eco-system/RAGE

Abstract

The massive student participation in Computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students' participation using two models – a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students' pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 ¼ :796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.