Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors
Structure-Based Discriminative Matrix Factorization for Detecting Inefficient Learning Behaviors
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DOI：
10.1109/wiiat50758.2020.00041
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
M. Mirzaei;Shaghayegh Sherry Sahebi;Peter Brusilovsky
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文献类型：
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作者：
M. Mirzaei;Shaghayegh Sherry Sahebi;Peter Brusilovsky
现代在线学习平台提供了丰富的学习内容，同时将学习和实践内容的选择留给学习者。最近的研究表明，许多学生使用低效的学习策略，导致在这方面的表现较低。通过监测学习数据来检测低效学习行为的能力为及时干预开辟了一条道路，这可能会导致更好的学习和表现。在这项工作中，我们提出了SB-DNMF，一个基于结构的判别非负矩阵分解模型，旨在区分低学习增益和高学习增益学生的共同和不同的学习行为模式。我们的模型可以发现潜在的群体的学生的行为微模式，同时占这些微模式之间的结构相似性的基础上加权编辑距离措施。我们的实验表明，SB-DNMF可以找到有意义的潜在因素，与学生的学习增益，并可以聚类的行为模式到共同的（特质），和性能相关的群体。
Modern online learning platforms offer a wealth of learning content while leaving the choice of content for study and practice to the learner. Recent work has demonstrated that many students use inefficient learning strategies that lead to lower performance in this context. The ability to detect inefficient learning behavior by monitoring learning data opens a way to timely intervention that could lead to better learning and performance. In this work, we propose SB-DNMF, a structure-based discriminative non-negative matrix factorization model aimed to distinguish between common and distinct learning behavior patterns of low- and high-learning gain students. Our model can discover latent groups of students’ behavioral micro-patterns while accounting for the structural similarities between these micro-patterns based upon a weighted edit-distance measure. Our experiments demonstrate that SB-DNMF can find meaningful latent factors that are associated with students’ learning gain and can cluster the behavioral patterns into common (trait), and performance-related groups.