Expert assistant for developing and maintaining a German educational evaluation dashboard built with Streamlit for analyzing IQES school quality data with AI-powered insights.
Expert guidance for working with a professional German educational evaluation dashboard that analyzes IQES (Instrumente für die Qualitätsentwicklung und Evaluation an Schulen) school quality data using Streamlit and AI-powered insights.
This is a specialized application for German educational institutions that processes IQES evaluation data. The dashboard provides comprehensive analysis of school quality metrics, comparing different educational programs (BM - Büromanagement vs VK - Veranstaltungskaufleute) and generating AI-powered recommendations.
**Key technologies:** Streamlit, pandas, plotly, scikit-learn, OpenAI API, openpyxl
1. **SchulqualitätsDashboard** (`dashboard.py:109`)
- Main dashboard class with IQES-specific data processing
- Handles Excel file parsing with proper IQES structure recognition
- Processes multi-sheet evaluations (Antwortskala, Offene Frage, Einfachauswahl)
2. **KI_Schulqualitäts_Analyzer** (`dashboard.py:492`)
- AI-powered analysis engine
- German text processing with sentiment analysis
- OpenAI integration for advanced insights
The application processes standard IQES Excel exports with specific sheet types:
- Scale: 1 = "trifft nicht zu" to 4 = "trifft zu"
- Column J: Pre-calculated averages (primary metric)
- Column K: Response counts (N values)
- Columns B,D,F,H: Response distribution counts
- German sentiment analysis
- Keyword extraction with German stopwords
**Quick start:**
```bash
chmod +x start_dashboard.sh
./start_dashboard.sh
```
**Manual setup:**
```bash
./install_requirements.sh
streamlit run dashboard.py
```
**Enable AI features (optional):**
```bash
export OPENAI_API_KEY='your-api-key'
streamlit run dashboard.py
```
```
streamlit>=1.28.0 # Web framework
pandas>=1.5.0 # IQES data processing
plotly>=5.15.0 # Interactive visualizations
scikit-learn>=1.3.0 # ML clustering
openai>=1.0.0 # German text analysis (optional)
openpyxl>=3.1.0 # Excel file processing
```
1. **Maintain German localization** - All UI text, labels, and outputs must be in German
2. **Respect IQES data structure** - Never assume column positions; always use column J for averages, column K for N values
3. **Handle malformed files gracefully** - Include try/except blocks with user-friendly German error messages
4. **Preserve professional styling** - Follow existing CSS patterns for educational reporting standards
Always wrap IQES file processing in error handlers:
```python
try:
# IQES data processing
except Exception as e:
st.error(f"Fehler beim Verarbeiten der IQES-Datei: {str(e)}")
```
When working with text analysis:
1. Create new method in `SchulqualitätsDashboard` class
2. Follow IQES column mapping (J=average, K=count)
3. Add German UI labels in Streamlit sidebar
4. Update CSS if new visualization types are needed
1. Edit `KI_Schulqualitäts_Analyzer.generiere_handlungsempfehlungen()`
2. Adjust critical thresholds (currently <2.5 for high priority)
3. Update German prompt templates for OpenAI calls
4. Test with real IQES data files
1. Use plotly for consistency
2. Apply German labels and tooltips
3. Use professional color scheme (matching existing charts)
4. Ensure mobile responsiveness
1. **Always test with real IQES files** before deploying changes
2. **Preserve backward compatibility** with existing IQES export formats
3. **Document German terminology** when adding new features
4. **Consider educational context** in all UI/UX decisions
5. **Optimize for non-technical users** (school administrators)
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