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.
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
- University College London í(UCL), John Shawe-Taylor
- Institut “Jožef Stefan” (JSI), John Shawe-Taylor
- INESC TEC, Alipio Jorge
Primary Contact: John Shawe-Taylor, UCL
Main results of micro project:
Adding humanly-intuitive model assumptions to the TrueLearn Bayesian learner model such as 1) interest, 2) knowledge of learner 3) semantic relatedness between content topics has been achieved successfully leading to improved predictive performance. A dataset of personalised learning pathways of over 20000 learners has been composed and under review for publication. Analysis on Optimal Transport for generating interpretable narratives using Earth Mover's Distance (EMD) of Wikipedia concepts also showed promise in scenarios where there is a limited number of topic annotations per document. A novel method for cross-lingual information retrieval using EMD has been invented pursuing this idea. Incorporating semantic networks (WordNet, WikiData) in building higher-level reasoning for recommendation also shows promise albeit with limited results at this point. Successful expansion of WordNet network using WikiData network is achieved. The resultant semantic network indicates that the quality of reasoning over Wiki Annotated video lectures can be improved in this way.
Contribution to the objectives of HumaneAI-net WPs
The main contributions are towards WP1 and WP3. In terms of WP1, improvements made to TrueLearn contribute towards building a much richer representation of the learner in the educational recommender. This builds towards linking symbolic and subsymbolic AI where the representation is understood by AI while it can be easily translated to a humanly-intuitive narrative. The proposed online learning scheme connects with continual learning where the model constantly updates itself on a lifelong basis. EMD and semantic network enrichment also further contribute to human-in-the-loop machine learning by empowering the human user in the recommendation process with potential narratives of learning trajectories. The results also connect to ideas in WP3 as the recommender’s foundations are built on user modelling and exploiting user interaction history. Having already built a rich representation, the results present opportunities to further contribute to identifying useful visualisations and human-AI collaboration frameworks to utilising the learner model in-the-wild.
- Publication: X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI – Maria Perez-Ortiz
- Publication: Understanding the Relevance Scores in Cross-Lingual Document Retrieval – Erik Novak
Submitted to International Journal for Information Processing & Management
- Publication: Towards Semantically Aware Educational Recommenders – Sahan Bulathwela
To be Submitted.
- Publication: TrueLearn IK: Modelling Interests and Knowledge of Online Learners – Sahan Bulathwela
To be Submitted to EAAI’22
- Publication: PEEK: A Large Dataset of Learner Engagement with Educational Videos – Sahan Bulathwela
Submitted to ORSUM workshop, RecSys’21
- Dataset: PEEK Dataset – Sahan Bulathwela
- Program/code: TrueLearn Model – Sahan Bulathwela
- Program/code: Semantic Networks for Narratives – Daniel Loureiro