Investigating Transitions in Affect and Activities for Online Learning Interventions

Abstract

In this paper we investigate d student online learning and non -learning related activities. The data collected in the research showed th at students felt certain affe ctive states when performing particular activity types and perform ed particular activity types when they felt certain affective states. These transitions were further investigated by generating transition likelihoods between al l pairs of activity types and affective states . The t ransition likelihoods were used to create a model that could predict possible student behavior when they learn online . Certain t ransitions where in students may need interventions were identified , so that feedback can be put in place to prevent them from transitioning to activity types and affective states that do not support learning .