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Leveraging Multi-Source Data Fusion Approach for Fine-Grained Affective-Appraisal Analysis in TPD-Oriented Online

Di Chen¹, Xinyue Xu¹, Ruiyang Gao¹

¹Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.

Behavioral Sciences (Basel, Switzerland)

|June 26, 2026

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This study introduces a new framework for analyzing teacher emotions in online professional development, enhancing learning insights by combining text and interaction data for better affective-appraisal analytics.

Area of Science:

Educational Technology
Affective Computing
Human-Computer Interaction

Background:

Online platforms are increasingly used for teacher professional development (TPD).
Analyzing teacher emotions in TPD is challenging due to professional discourse norms.
Existing emotion analysis methods are insufficient for nuanced TPD contexts.

Purpose of the Study:

To develop a fine-grained, low-intrusion affective-appraisal analysis framework for online TPD.
To integrate textual data with platform interaction logs for comprehensive analysis.
To capture teachers' perceived usefulness and learning gains alongside emotional states.

Main Methods:

Utilized Bidirectional Encoder Representations from Transformers (BERT) for text representation.

Keywords:

affective computing fine-grained affective-appraisal analysis multi-source data fusion pre-service teacher professional learning teacher professional development

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Employed intra-week Long Short-Term Memory (LSTM) for sequential data aggregation.

Integrated interpretable behavioral-log features with text-based analysis via feature-level fusion.

Applied a four-dimensional affective-appraisal model (pleasure, arousal, dominance, utility).

Main Results:

Intra-week sequential modeling significantly improved performance (Macro-F1) over baseline and non-sequential models.
Integrating interaction logs enhanced accuracy across all dimensions, providing process-based insights.
The framework demonstrated effectiveness in analyzing affective appraisals in pre-service teacher learning.

Conclusions:

The proposed framework offers a scalable and context-sensitive approach to affective-appraisal analytics in online TPD.
Combining textual and interaction data provides a more holistic understanding of teacher learning experiences.
This method advances the analysis of emotions within professional learning environments.