ringo380 / ringo380/QueryGrade
Personalized Learning Paths
Open
Nobody has claimed this yet.
area-ml-system
blocked-on-real-usage
priority-medium
type-feature
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
Goal
Implement skill-based progression tracking and generate adaptive learning recommendations.
Proposed Improvements
- Track user query patterns and skill progression
- Generate personalized improvement recommendations
- Create topic-based knowledge assessments
- Build interactive SQL tutorials based on user queries
Success Metrics
- User skill level classification accuracy >85%
- Recommendation relevance rating >4/5
- Learning path completion rate >60%
Related Components
analyzer/ml/recommendations/learning_paths.py(waslearning_path_generator.py)analyzer/ml/recommendations/personalization_engine.py(wasfeedback_personalization.py)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading analyzer/ml/recommendations/learning_paths.py and analyzer/ml/recommendations/personalization_engine.py, including their current responsibilities. Map the proposed progression tracking, assessments, recommendations, and interactive SQL tutorials to those components. Done should include the listed capabilities and evaluation against the stated accuracy, relevance, and completion-rate metrics.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, sql
- Domain
- databases, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100