Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students' knowledge gaps and recommending learning materials to them. Current student knowledge modeling techniques mostly rely on one type of learning material, mainly problems, to model student knowledge growth. These approaches ignore the fact that students also learn from other types of material. In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types. Our multi-view knowledge model (MVKM) incorporates a flexible knowledge increase objective on top of a multi-view tensor factorization to capture occasional forgetting while representing student knowledge and learning material concepts in a lower-dimensional latent space. We evaluate our model in different experiments toshow that it can accurately predict students' future performance, differentiate between knowledge gain in different student groups and concepts, and unveil hidden similarities across learning materials of different types.

中文翻译：

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从多种学习资源类型建模知识获取

学生在与各种学习材料（例如视频讲座、问题和讨论）互动时获得知识。对学生在学习期间每个点的知识进行建模，了解每种学习材料对学生知识的贡献，对于检测学生的知识差距并向他们推荐学习材料至关重要。当前的学生知识建模技术大多依赖于一种学习材料，主要是问题，来对学生的知识增长进行建模。这些方法忽略了学生也从其他类型的材料中学习的事实。在本文中，我们提出了一种学生知识模型，该模型可以捕捉从多种学习资源类型中学习所带来的知识增长，同时揭示不同类型学习材料之间的关联。我们的多视图知识模型 (MVKM) 在多视图张量分解之上结合了灵活的知识增长目标，以捕获偶尔的遗忘，同时在低维潜在空间中表示学生知识和学习材料概念。我们在不同的实验中评估我们的模型，以表明它可以准确地预测学生未来的表现，区分不同学生群体和概念的知识获取，并揭示不同类型学习材料之间隐藏的相似之处。