Enhancing Affect Detection in Game-Based Learning Environments with Multimodal Conditional Generative Modeling

Accurately detecting and responding to student affect is a critical capability for adaptive learning environments. Recent years have seen growing interest in modeling student affect with multimodal sensor data. A key challenge in multimodal affect detection is dealing with data loss due to noisy, missing, or invalid multimodal features. Because multimodal affect detection often requires large quantities of data, data loss can have a strong, adverse impact on affect detector performance. To address this issue, we present a multimodal data imputation framework that utilizes conditional generative models to automatically impute posture and interaction log data from student interactions with a game-based learning environment for emergency medical training. We investigate two generative models, a Conditional Generative Adversarial Network (C-GAN) and a Conditional Variational Autoencoder (C-VAE), that are trained using a modality that has undergone varying levels of artificial data masking. The generative models are conditioned on the corresponding intact modality, enabling the data imputation process to capture the interaction between the concurrent modalities. We examine the effectiveness of the conditional generative models on imputation accuracy and its impact on the performance of affect detection. Each imputation model is evaluated using varying amounts of artificial data masking to determine how the data missingness impacts the performance of each imputation method. Results based on the modalities captured from students? interactions with the game-based learning environment indicate that deep conditional generative models within a multimodal data imputation framework yield significant benefits compared to baseline imputation techniques in terms of both imputation accuracy and affective detector performance.