  Collaborative learning is an educational approach that enhances learning
through shared goals and working together. Interaction and regulation are two
essential factors related to the success of collaborative learning. Since the
information from various modalities can reflect the quality of collaboration, a
new multimodal dataset with cognitive and emotional triggers is introduced in
this paper to explore how regulations affect interactions during the
collaborative process. Specifically, a learning task with intentional
interventions is designed and assigned to high school students aged 15 years
old (N=81) in average. Multimodal signals, including video, Kinect, audio, and
physiological data, are collected and exploited to study regulations in
collaborative learning in terms of individual-participant-single-modality,
individual-participant-multiple-modality, and
multiple-participant-multiple-modality. Analysis of annotated emotions, body
gestures, and their interactions indicates that our multimodal dataset with
designed treatments could effectively examine moments of regulation in
collaborative learning. In addition, preliminary experiments based on baseline
models suggest that the dataset provides a challenging in-the-wild scenario,
which could further contribute to the fields of education and affective
computing.
