Because of the growing recognition of the role that emotion and motivation play in learning, affective computing has become the subject of increasing attention in research on interactive learning environments. Narrative-centered learning environments, which are game-based learning environments where learning activities play out in dynamically generated interactive narratives, afford great opportunity for exploring computational models of student affect. The project will explore student affect modeling through the design, implementation, and evaluation of affect models for game-based learning environments. It will develop 1) affect recognition technologies to predict students' affect states, recognize engagement and flow, and detect frustration, and 2) affect expression technologies to customize pedagogical activities and dynamically plan the empathetic responses of the virtual agents in the learning environment. All design, implementation, and evaluation activities will be carried out in Crystal Island, a narrative-centered game-based learning environment for biology.
The project will determine precisely which affect modeling techniques best close the 'affective loop' and contribute most effectively to student learning effectiveness and motivation. It is expected that the project will have a significant impact on the theory and practice of educational technology. Because of the multidisciplinary nature of the research objectives, the project will produce significant advances in computational models of affect recognition and affect expression. It is anticipated that the resulting computational models of student affect and the cognitive account of affect-informed interaction in game-based learning environments will create new learning environment technologies that promote high levels of achievement and find broad application in science education.
Because of the growing recognition of the role that affect plays in learning, affective computing has become the subject of increasing attention in research on interactive learning environments. Game-based learning environments present a significant opportunity to investigate student affect in interactive learning. In particular, narrative-centered game-based learning environments, which are game-based learning environments where learning activities play out in dynamically generated interactive narratives and training scenarios, afford great opportunity for exploring computational models of student affect. This project laid the foundation for affective computing in game-based learning. In particular, it focused on the development of new computational techniques for automatically acquiring models of affect from behavior data logs of students’ interactions with a game-based learning environment. This line of investigation included preliminary analyses of student affect state transitions, the development of theoretically grounded models represented with dynamic Bayesian networks, and the exploration of personality profiles to support affect modeling. The project also devised techniques for modeling empathy in virtual pedagogical agents, and it designed affect-informed computational models to provide feedback to students. Through the new computational models and techniques developed in the project, it is anticipated that increasingly effective learning technologies utilizing these models of student affect will be developed to promote high levels of achievement and find broad application in science education.
