Requirement-Paper - offene technologische Entwicklungen

AP1 – User behavior tracking

User behavior is being successfully tracked at the individual level using xAPI, allowing for detailed insights into personal learning interactions. This supports personalized feedback and analytics aligned with the project goals.

However, tracking groups of users is no longer feasible due to challenges in grouping heterogeneous user profiles, lack of a consistent grouping model, and concerns related to privacy and data ethics. As a result, group-level behavior tracking has been excluded from the current implementation.

AP4 – Configuration / Extension of xAPI / LRS (Statements, Queries, Activities, Metadata)

The current work on AP4 has focused on enhancing the visualization of learning activity data collected through xAPI. Several reports of xAPI statements were implemented with different charts, providing an initial overview of user activity, learning progress, and competence areas. Although the initial idea was to implement some of these visualizations directly within the LRS, they were ultimately integrated into the LXP prototype instead. This decision was made to offer a more user-friendly interface and facilitate easier access and interaction for end users.

The implementation of a complete visualization of the user journey was initiated, aiming to map out learners’ paths through activities and interactions within the LXP. However, initial testing revealed significant challenges in determining which data points were most relevant and how to present them in a meaningful and user-friendly way. Due to the complexity and ambiguity around visualization approaches, and considering project priorities, this feature was deprioritized. As a result, the development of a fully functional and usable version of the user journey visualization was halted.

AP6 - Implementierung Workflow Anpassungen (Statements/Meta Schemata)

For AP6, the implementation of workflow adjustments included extending xAPI statements by adding schema.org metadata to enrich the behavior tracking data. Although this enhanced data is stored within the system, it has not been actively utilized in the project so far. Initial plans also included integrating with the Nationale-Bildungsplattform (NBP) and the Allgemeines Metadatenprofil für Bildungsressourcen (AMB), which is derived from LOMS standards. However, during the course of the project, it was decided that these integrations were not essential, and no further development was pursued in this area.

AP8 - Design & Implementation Semantic Search & On-site Search API

For AP8, the design and implementation of semantic search and on-site search APIs focused on integrating OpenSearch with embeddings and k-nearest neighbors (kNN) search for ESCO skills and learning opportunities. This setup enables the LXP to identify semantically similar skills and opportunities, forming the foundation for the recommendation and user engagement engines. Although the initial plan included using embedding-based semantic search for learning opportunities, early tests showed that non-semantic search generally produced better results. Consequently, semantic search was not adopted as a full replacement across the LXP and was integrated only in some autocomplete components.

AP9 - Predictive Analitics Dashboard

In AP9, the Predictive Analytics Dashboard was implemented as a user interface component within the LXP prototype, designed to support learners in identifying skill gaps and receiving personalized learning recommendations. The dashboard provides visual insights based on user activity and profile data, helping individuals understand their progress and make informed decisions about what to learn next.

Although the original plan included advanced predictive analytics tools for LXP operators and content creators, time constraints and technical complexity led to a shift in focus towards end-user functionality. Nonetheless, several analytics charts and visual elements have been made available on the operator side to support monitoring and decision-making at a higher level.

AP11 - User Engagement Engine

In AP11, the original goal was to set up triggers to boost user engagement through real-time notifications about recommendations, new learning content, and comparative performance data (e.g., performing better than average). Additionally, the plan included developing an engagement engine with a user-friendly interface to enable AI-driven interactions with learners.

Instead of the initial approach, we implemented automated H5P quizzes with ranking features to foster engagement. Later, this was expanded by integrating a Retrieval Augmented Generation system based on a large language model (LLM) AI tutor and assistant. However, due to time constraints, real-time integration of xAPI performance data with the AI tutor is still pending.