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Updated: May 9, 2026

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

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Published on: September 27, 2020

Pri-MCCD: A Real-World Multimodal Dataset for Analyzing Classroom Climate in Primary School Lessons.

Weigang Lu¹, Ying Chen¹, Xiaofang Wang²

¹Department of Education, Ocean University of China, Songling Road No.238, Qingdao, 266100, Shandong, China.

Scientific Data

|May 7, 2026

Summary

This summary is machine-generated.

This study introduces Pri-MCCD, a new dataset capturing real primary school classroom dynamics. It aids AI in understanding complex learning environments and student emotions.

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Educational Technology
Artificial Intelligence
Affective Computing

Background:

AI struggles to perceive complex classroom dynamics like experienced teachers.
Understanding classroom climate is crucial for teaching effectiveness and student development, especially in primary schools.
Existing datasets lack real-world multimodal interactions and comprehensive climate annotations.

Purpose of the Study:

To present Pri-MCCD, a novel multimodal dataset for primary school classroom climate.
To support research in classroom diagnostics, affect-aware systems, and learning environment analysis.
To overcome limitations of existing task-specific, single-modal, and controlled-environment datasets.

Main Methods:

Collected data from fifteen 40-minute real-world primary school lessons.
Integrated visual (body posture) and auditory (acoustic cues) features.
Focused on capturing authentic multimodal interactions and broader classroom climate.

Main Results:

Developed Pri-MCCD, a dataset reflecting authentic classroom dynamics.
Dataset includes visual and auditory features from real primary school settings.
Annotations are designed to represent the broader classroom climate.

Conclusions:

Pri-MCCD addresses limitations of current datasets for classroom climate research.
The dataset enables advancements in AI's ability to understand learning environments.
Facilitates research in affect-aware systems and educational diagnostics.