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Research on Emotion Recognition Method Based on Adaptive Window and Fine-Grained Features in MOOC Learning.

Xianhao Shen¹, Jindi Bao¹, Xiaomei Tao²

¹School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This study introduces a new method to detect learner emotions in Massive Open Online Courses (MOOCs) by analyzing eye movement and scene features. The approach improves emotion recognition accuracy, benefiting online learning experiences.

Keywords:

adaptive window audio and visual features emotion recognition eye movement signal fine-grained feature

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Area of Science:

Educational Technology
Affective Computing
Human-Computer Interaction

Background:

Learner emotions significantly influence educational outcomes in Massive Open Online Courses (MOOCs).
Identifying subtle learner emotions during online learning is a persistent challenge.

Purpose of the Study:

To propose and evaluate a novel method for identifying learner emotions in MOOCs.
To address the difficulty in recognizing unobvious learner emotions by integrating multimodal features.

Main Methods:

A new method combining eye movement features and scene features for emotion identification.
Utilizing an adaptive window for sample partitioning to enrich eye movement data.
Employing fine-grained feature extraction within adaptive windows to enhance sample discrimination.

Main Results:

Achieved a 65.1% accuracy in four-category emotion recognition using only eye movement data in MOOC scenarios.
Demonstrated the effectiveness of adaptive window partitioning and fine-grained feature extraction for eye movement signals.

Conclusions:

The proposed method effectively identifies learner emotions in MOOCs.
Both adaptive windowing and fine-grained feature extraction techniques are valuable for analyzing eye movement signals and can be extended to other data modalities.