Negative emotional dynamics shape cognition and performance with MetaTutor: Toward building affect-aware systems

Abstract

Significant efforts are currently being made to design affect-aware systems to classify, monitor, and scaffold emotional experiences across a range of settings. However, most investigations are limited in their view of emotions due to less sophisticated methodologies and analytical techniques. To address these issues, we captured 174 undergraduates’ emotions over time and defined them by multiple dimensions: (1) temporality, (2) valence, and (3) activation during learning with an intelligent tutoring system called MetaTutor. Latent growth models revealed the stability of negative activating emotions over time was related to performance, and changes in negative deactivating emotions were related to time engaging in cognitive strategies. Finally, a random forest classifier revealed high accuracy in predicting high (top 30%) and low performance groups (bottom 30%) using pre-test scores, changes in negative deactivating emotions, and time engaging in cognitive strategies. These findings have important implications for designing affect-aware systems that can potentially leverage emotion interventions based on if, when, and how an emotion changed (or remained stable) to optimize cognition and performance with emerging technologies.