Through this work, we explore novel and advanced learner representation models aimed at exploiting learning trajectories to build an transparent, personalised and efficient automatic learning tutor through resource recommendations. We elaborate on the different types of publicly available data sources that can be used to build an accurate trajectory graph of how knowledge should be taught to learners to fulfil their learning goals effectively. Our aim is to capture and utilise the inferred learner state and the understanding the model has about sensible learning trajectories to generate personalised narratives that will allow the system to rationalise the educational recommendations provided to individual learners. Since an educational path consists heavily of building/following a narrative, a properly-constructed narrative structure and representation is paramount
to the problem of building successful and transparent educational recommenders.

Output

Paper on development of narrative representations for learning

Enhancements of the X5Learn portal for accessing Open Educational Resources (OER)

Visualisation software for landscapes of learning

User evaluations of software

Project Partners:

  • UCL, John Shawe-Taylor
  • UCL, John Shawe-Taylor
  • IJS, John Shawe-Taylor
  • UPorto, Alipio Jorge

Primary Contact: John Shawe-Taylor, UCL