Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses
Iterative Discriminant Tensor Factorization for Behavior Comparison in Massive Open Online Courses
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DOI：
10.1145/3308558.3313713
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发表时间：
2019-05
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影响因子：
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通讯作者：
Xidao Wen;Y. Lin;Xi Liu;Peter Brusilovsky;Jordan Barria-Pineda
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文献类型：
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作者：
Xidao Wen;Y. Lin;Xi Liu;Peter Brusilovsky;Jordan Barria-Pineda
越来越多地利用大规模在线开放课程，大大扩大了全球接受正规教育的机会。尽管该技术前景广阔，但学生在MOOC上的互动仍然是一个相对未被充分探索和理解的话题。本文提出了一种基于分层判别张量分解的多层次模式发现方法。我们制定的问题作为一个分层判别子空间学习问题，其中的目标是发现共享和歧视模式的层次结构。发现的模式，使两个性能组的对比行为的更有效的探索。我们在几个真实世界的MOOC数据集上进行了广泛的实验，以证明我们所提出的方法的有效性。我们的研究通过提供更多可解释的行为模式并将其与绩效结果的关系联系起来，推进了MOOC当前的预测建模。
The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.