Students' Emotional Self-Labels for Personalized Models

Abstract

There arc some implementations towards understanding students' emotional states through automated systems with machine learning models. However, generic Al models of emotions lack enough accuracy to autonomously and meaningfully trigger any interventions. Collecting self-labels from students as they assess their internal states can be a way to collect labeled subject specific data necessary to obtain personalized emotional engagement models. In this paper, we outline preliminary analysis on emotional self-labels collected from students while using a learning platform.