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.

本研究旨在通过 Integrative approach 构建一个新颖的 TrueLearn 教育推荐系统，该系统由多种学习参考驱动，可以同时考虑学习者的知识和内容的新颖性，并保持长期时间的准确学习者代表性。

面向终身学习者的综合教育推荐系统

Towards an Integrative Educational Recommender for Lifelong Learners

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.

本文提出了一种动态、可扩展、透明的教育推荐系统，基于 Wikipedia 的文本本体自动提取教育资源的知识组件及启发于项目反应理论和知识追踪的一组在线贝叶斯策略。我们的提案 TrueLearn 专注于向具有足够背景知识并能够理解和学习材料的学习者推荐具备足够新颖性的材料，以帮助学习者提高对主题的知识并保持他们的主动性。我们还构建了一个大型的开放式教育视频讲座数据集，并测试了所提算法的性能，结果显示出建立有效的教育推荐系统的明显前景。