Automatic classification of subjective attributes from student messages in virtual learning environments

Abstract

Accompanying students in virtual learning environments to identify those who need help is a difficult and time-consuming task. Identifying subjective attributes that recognize students’ feelings can help teachers and tutors in pedagogical interventions. The interaction must motivate and keep students engaged. This article proposes an architecture capable of automatically carrying out some pedagogical intervention. The architecture uses automatic textual classification models to detect multiple attributes in post messages, such as Sentiment, Post Type, Urgency, and Confusion. The main goal is to predict learning problems and act to minimize their impacts. The evaluation was performed with data from the Stanford MOOCPosts Dataset to verify if the models allow the automatic classification of subjective attributes. Our results show that the proposal outperforms other approaches in this dataset.