Affective Dynamics and Cognition during Game-based Learning

Elizabeth B. Cloude

Daryn A. Dever

Debbie L. Hahs-Vaughn

Andrew J. Emerson

Roger Azevedo

James Lester

Inability to regulate affective states can impact one’s capacity to engage in higher-order thinking like scientific reasoning with game-based learning environments. Many efforts have been made to build affect-aware systems to mitigate the potentially detrimental effects of negative affect. Yet, gaps in research exist since accurately capturing and modeling affect as a state that changes dynamically over time is methodologically and analytically challenging.

In this paper, we calculated multilevel mixed effects growth models to assess whether seventy-eight participants’ ( n = 78) time engaging in scientific reasoning (via logfiles) were related to time facially expressing confused, frustrated, and neutral states (via facial recognition software) during game-based learning with Crystal Island.

The fitted model estimated significant positive relations between the time learners facially expressed confusion, frustration, and neutral states and time engaging in scientific-reasoning actions. The time individual learners facially expressed frustrated, confused, and neutral states explained a significant amount of variation in time engaging in scientific reasoning.

Our finding emphasize that individual differences and agency may play a important role on relations between affective states, their dynamics, and higher-order cognition during game-based learning. Designing affect-aware game-based learning environments that track the dynamics within individual learners’ affective states may best support cognition.

This publication uses Facial Expression Analysis which is fully integrated into iMotions Lab

Learn more