Gender-sensitive sentiment analysis for estimating the emotional climate in online teacher education.

Abstract

Teacher training takes place in distance education to a large extent. Within these contexts, trainers should make use of all the information available to adapt and refine their instructional methods during the training process. Sentiment analysis (SA) can give immediate feedback of the emotions expressed and help in the training process, although it has been used infrequently in educational settings, slow to assess, and bound to interpretative issues, such as gender bias. This research aimed to design and evaluate a SA gender-sensitive method as a proxy to characterize the emotional climate of teacher trainees in an online course. An explanatory case study with mixed methods was implemented among students of the Interuniversity Master of Educational Technologies (N = 48). Participants' messages were analyzed and correlated with learning achievement and, along with a qualitative study of participants' satisfaction with the Master's degree, to validate the effectiveness of the method. Results show that sentiment expression cannot be used to exactly predict participants' achievement, but it can guide trainers to foresee how participants will broadly act in a learning task and, in consequence, use SA results for tuning and improving the quality of the guidance during the course. Gender differences found in our study support gendered patterns related to the emotional climate, with female participants posting more negative messages than their counterparts. Last but not least, the design of well-adjusted teaching-learning sequences with appropriate scaffolding can contribute to building a positive climate in the online learning environment.