Sahan is affiliated to the UCL Centre for Artificial Intelligence currently contributing to the X5GON project working with Prof. Emine Yilmaz and Prof. John Shawe-Taylor. His research interests lie on the theme: “Improving Recommendations of Educational Contents to Lifelong Learners”. Before joining UCL, he worked in several research roles in the industry in cybersecurity and personalised advertising domains where he gained experience in user state modelling in a big data landscape.
PhD in Artificial Intelligence, Ongoing
University College London, UK
MSc in Computational Statistics and Machine Learning, 2014
University College London, UK
PGDip in Applied Statistics, 2012
University of Peradeniya, Sri Lanka
BSc (Hons.) in Computing, 2010
University of Portsmouth, UK
X5GON project (https://www.x5gon.org) envisions to leverage a Cross Modal, Cross Cultural, Cross Lingual, Cross Domain, and Cross Site Global Open Educational Resource Network for informal learners.
Main responsibilities include: - Conducting deep research and execute well designed experiments leading to inventing novel methods in improving informal learners’ learning trajectories. - Deriving automatic quality assessment models for educational resources - Deriving rich representations of knowledge and its learners - Deriving intelligent models for personalized recommendation of educational materials.
This paper introduces an interface that enables the user to quickly identify relevant fragments within multiple long documents. The proposed method relies on a machine-generated layer of annotations that reveals the coverage of topics per fragment and document. To illustrate how the annotations double as a tool for preview as well as navigation, an example application is presented in the form of a personalised learning system that recommends relevant fragments of video lectures according to user’s history. Potential implications of this approach for lifelong learning are discussed. We argue that this approach is generally applicable to recommender and information retrieval systems, across multiple knowledge domains and document types.
One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.
The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowledge states of masses of learners for significantly longer periods of time (ideally, a lifetime). This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner’s knowledge from implicit data in the form of engagement with open educational resources. We i) use a text ontology based on Wikipedia to automatically extract knowledge components of educational resources and, ii) propose a set of online Bayesian strategies inspired by the well-known areas of item response theory and knowledge tracing. Our proposal, TrueLearn, focuses on recommendations for which the learner has enough background knowledge (so they are able to understand and learn from the material), and the material has enough novelty that would help the learner improve their knowledge about the subject and keep them engaged. We further construct a large open educational video lectures dataset and test the performance of the proposed algorithms, which show clear promise towards building an effective educational recommendation system.