TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
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DOI：
10.1609/aaai.v34i01.5395
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发表时间：
2019-11
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影响因子：
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通讯作者：
Sahan Bulathwela;M. Pérez-Ortiz;Emine Yilmaz;J. Shawe-Taylor
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作者：
Sahan Bulathwela;M. Pérez-Ortiz;Emine Yilmaz;J. Shawe-Taylor
计算机辅助学习系统的最新进展和今天开放教育资源的可用性，为广大学习者提供具有成本效益的高质量教育提供了一条途径。计算机辅助学习最雄心勃勃的用例之一是建立终身学习推荐系统。与短期课程不同，终身学习提出了独特的挑战，需要复杂的推荐模型，这些模型考虑了广泛的因素，如学习者的背景知识或材料的新奇，同时有效地保持大量学习者的知识状态，时间要长得多（理想情况下，一生）。这项工作为建立一个动态，可扩展和透明的教育推荐系统奠定了基础，以开放教育资源的形式从隐式数据中建模学习者的知识。我们i）使用基于维基百科的文本本体自动提取教育资源的知识成分，ii）提出了一套在线贝叶斯策略的启发，著名的领域的项目反应理论和知识追踪。我们的建议，TrueLearn，侧重于学习者有足够的背景知识的建议（因此他们能够理解并从材料中学习），并且材料具有足够的新奇，可以帮助学习者提高他们对该主题的知识并保持他们的参与。我们进一步构建了一个大型的开放式教育视频讲座数据集，并测试了所提出的算法的性能，这表明了建立一个有效的教育推荐系统的明确承诺。
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.