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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LRS User Journey Visualizer

The LRS User Journey Visualizer project aimed to develop a tool for displaying user journeys in a simple and effective manner based on interactions with learning content. By visualizing user flows, the tool intended to provide content providers and platform administrators with valuable insights into user behavior and learning patterns. The project explored multiple visualization approaches derived from xAPI statements to represent these journeys.

Although various visualization techniques were tested in the prototype phase, none were deemed sufficiently effective to achieve the project's objectives. Additionally, plans to extend LRS functionalities were deprioritized due to time constraints and their relatively low impact on the core goals. Despite these challenges, the project provided valuable lessons and highlighted the complexities involved in representing user journeys, offering a foundation for future work in this area.

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Last updated 4 months ago