EduPLEx_API
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Recommendation, reporting & analytics
Recommendation, reporting & analytics
  • Experiments report
    • Key concepts
    • Data sources
    • First demonstrator: ESCO ontologies and semantic matching
    • Software design
      • Endpoints Sbert_eduplex
      • Setup Sbert_eduplex
    • AI Applications
    • Conclusions
    • Recommendation
    • Bibliography
  • Recommendation Engine
  • Reporting and predictive analytics
  • LRS User Journey Visualizer
  • AI Tutor - RAG system
    • LLM-augmented Retrieval and Ranking for Course Recommendations
    • Retrieval of course candidates when searching via title.
    • Answer Generation Evaluation
    • Chunk Size and Retrieval Evaluation
    • Chunking Techniques – Splitters
    • Golden Case CLAPNQ
    • Comparative Retrieval Performance: Modules vs Golden Case
    • LLM-based Evaluator for Context Relevance
    • Retrieval Performance Indexing pdf vs xapi, and Keywords vs Questions
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Reporting and predictive analytics

The Reporting and Predictive Analytics project was designed to display key user metrics captured by the User Behavior Tracker with clear and understandable visualizations. These visualizations aim to assist content providers and platform administrators in understanding user interactions and engagement, enabling data-driven decisions to improve learning experiences.

The project successfully developed basic visualizations, demonstrating the value and utility of the tracked metrics. By providing an intuitive way to interpret user data, the project highlights the potential of leveraging analytics for optimizing content delivery and user engagement. This work establishes a foundation for further enhancements in reporting and predictive capabilities.

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Last updated 4 months ago