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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.
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.
Keywords:
adaptive windowaudio and visual featuresemotion recognitioneye movement signalfine-grained featureMore Related Videos
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