Pedagogy Analysis V2
Overview
Pedagogy Analysis V2 is a transformer-based model fine-tuned for analyzing student feedback about teachers, teaching methods, course delivery, classroom engagement, assessment practices, and overall learning experiences.
The model is designed to support educational analytics workflows by extracting sentiment and pedagogical insights from free-form student comments.
Intended Use
This model is intended for:
- Student course evaluations
- Teacher feedback analysis
- Educational quality monitoring
- Aspect-based sentiment analysis
- Institutional reporting and analytics
Example inputs:
The instructor explained difficult concepts clearly but moved too quickly through the practical examples.
Assignments were useful, although feedback on submissions was often delayed.
Model Details
| Property | Value |
|---|---|
| Model Name | Pedagogy Analysis V2 |
| Framework | Transformers |
| Format | SafeTensors |
| Language | English |
| Task | Educational sentiment analysis |
Training Data
The model was fine-tuned on a custom educational feedback dataset focused on:
- Teaching effectiveness
- Course organization
- Assessment quality
- Classroom engagement
- Communication
- Learning support
Limitations
The model may:
- Misinterpret highly ambiguous comments
- Struggle with domain-specific slang
- Produce lower accuracy on feedback substantially different from the training data
- Require human review for high-stakes evaluation decisions
The model should be used as a decision-support tool and not as the sole basis for personnel or academic decisions.
Usage
from transformers import AutoTokenizer, AutoModel
model_id = "chiraghariprasad/pedagogy-analysis-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)
Citation
If you use this model in research or production systems, please cite the repository:
Chirag Hariprasad. Pedagogy Analysis V2.
https://huggingface.co/chiraghariprasad/pedagogy-analysis-v2
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