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TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources | | Sahan Bulathwela
; Maria Perez-Ortiz
; Emine Yilmaz
; John Shawe-Taylor
; | | Date: |
21 Nov 2019 | | Abstract: | 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. | | Source: | arXiv, 1911.9471 | | Services: | Forum | Review | PDF | Favorites | |
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