# Exploring Interactions and Regulations in Collaborative Learning: An Interdisciplinary Multimodal Dataset

arXiv: https://arxiv.org/abs/2210.05419 · HTML: https://mlsanity.com/paper/2210.05419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Multi-attention Recurrent Network for Human Communication Comprehension](https://mlsanity.com/paper/1802.00923/) (2018)
- [Context Based Emotion Recognition using EMOTIC Dataset](https://mlsanity.com/paper/2003.13401/) (2020)
- [iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis](https://mlsanity.com/paper/2107.00285/) (2021)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise](https://mlsanity.com/paper/1409.5181/) (2014)
- [Learning Social Relation Traits from Face Images](https://mlsanity.com/paper/1509.03936/) (2015)
- [Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction](https://mlsanity.com/paper/1503.00688/) (2015)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition](https://mlsanity.com/paper/2106.03487/) (2021)
- [Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition](https://mlsanity.com/paper/2204.11053/) (2022)

## Abstract (arXiv)

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.