Integrating Artificial Intelligence into Research on Emotions and Behaviors in Science Education

Most research on emotions and behaviors in science education has used observational or declarative methods. These approaches present certain strengths, but they have important limitations for deepening our understanding of the affective domain. In this work, we develop a method for analyzing the dynamics of affective variables during an inquiry-based activity with an artificial intelligence system that recognizes facial expressions. Although the study was carried out on 12 students, here we analyze data from one person to describe the method in detail. The videos were processed with a software which outputs behavioral and emotional signals. To analyze them, we applied centered moving averages with different widths. This allowed us to align and interpret the dynamics of emotional, behavioral, and learning actions. We found spikes of Surprise when the student seemingly implemented their models, and their predictions were not met. Our analysis suggests the existence of four phases in the inquiry-based activity with specific dynamic profiles. This work lays the foundations for researchers and teachers to develop tools to monitor emotions and behaviors.