Accurately recognizing students’ affective states is critical for enabling adaptive learning environments to promote engagement and enhance learning outcomes. Multimodal approaches to student affect recognition capture multi-dimensional patterns of student behavior through the use of multiple data channels. An important factor in multimodal affect recognition is the context in which affect is experienced and exhibited. In this paper, we present a multimodal, multitask affect recognition framework that predicts students’ future affective states as auxiliary training tasks and uses prior affective states as input features to capture bi-directional affective dynamics and enhance the training of affect recognition models. Additionally, we investigate cross-stitch networks to maintain parameterized separation between shared and task-specific representations and task-specific uncertainty-weighted loss functions for contextual modeling of student affective states. We evaluate our approach using interaction and posture data captured from students engaged with a game-based learning environment for emergency medical training. Results indicate that the affective dynamics-based approach yields significant improvements in multimodal affect recognition across four different affective states.

Recent years have seen growing interest in modeling visitor engagement in museums with multimodal learning analytics. In parallel, there has also been growing concern about issues of fairness and encoded bias in machine learning models. In this paper, we investigate bias detection and mitigation techniques to address issues of algorithmic fairness in multimodal models of museum visitor visual attention. We employ slicing analysis using the Absolute Between-ROC Area (ABROCA) statistic to detect encoded bias present in multimodal models of visitor visual attention trained with facial expression and posture data from visitor interactions with a game-based museum exhibit about environmental sustainability. We investigate instances of gender bias that arise between different combinations of modalities across several machine learning techniques. We also measure the effectiveness of two different debiasing strategies—learned fair representations and reweighing—when applied to the trained multimodal visitor attention models. Results indicate that patterns of bias can arise across different modality combinations for the different visitor visual attention models, and there is often an inherent tradeoff between predictive accuracy and ABROCA. Analyses suggest that debiasing strategies tend to be more effective on multimodal models of visitor visual attention than their unimodal counterparts

Modeling visitor engagement is a key challenge in informal learning environments, such as museums and science centers. Devising predictive models of visitor engagement that accurately forecast salient features of visitor behavior, such as dwell time, holds significant potential for enabling adaptive learning environments and visitor analytics for museums and science centers. In this paper, we introduce a multimodal early prediction approach to modeling visitor engagement with interactive science museum exhibits. We utilize multimodal sensor data including eye gaze, facial expression, posture, and interaction log data captured during visitor interactions with an interactive museum exhibit for environmental science education, to induce predictive models of visitor dwell time. We investigate machine learning techniques (random forest, support vector machine, Lasso regression, gradient boosting trees, and multi-layer perceptron) to induce multimodal predictive models of visitor engagement with data from 85 museum visitors. Results from a series of ablation experiments suggest that incorporating additional modalities into predictive models of visitor engagement improves model accuracy. In addition, the models show improved predictive performance over time, demonstrating that increasingly accurate predictions of visitor dwell time can be achieved as more evidence becomes available from visitor interactions with interactive science museum exhibits. These findings highlight the efficacy of multimodal data for modeling museum exhibit visitor engagement.

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.

Utilizing sensors for affect detection in adaptive learning technologies has been the subject of growing interest in recent years. This extends to the collection of multiple concurrent sensor-based input channels to enable multimodal affective modeling. However, sensors pose significant challenges to affect detection, including sensor connectivity issues, background noise, inconsistent data logging, and loss of data due to hardware failure. In this paper, we introduce a framework for multimodal data imputation to improve automated detection of student affect in adaptive learning technologies. Through the use of an autoencoder neural network trained on Microsoft Kinect-based posture data and electrodermal activity data with synthetic noise injection, we approximate missing values within the original dataset while still preserving the inter-related context between features when reconstructing the dataset. The reconstructed dataset can be used in conjunction with multimodal data fusion techniques to further boost affect detector accuracy. Results indicate that this framework improves the effectiveness of multimodal affect detectors when compared to unimodal baseline models, as well as models using baseline data imputation techniques such as mean imputation. Further, it maintains cross-modality information that influences the multimodal affect detectors' performance, as the approach also outperforms previous work using the latent representation of the imputed dataset as training data instead of a complete reconstruction of the original dataset's dimensionality.