Detecting Trait versus Performance Student Behavioral Patterns Using Discriminative Non-Negative Matrix Factorization
Detecting Trait versus Performance Student Behavioral Patterns Using Discriminative Non-Negative Matrix Factorization
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发表时间：
2020
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通讯作者：
M. Mirzaei;Shaghayegh Sherry Sahebi;Peter Brusilovsky
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作者：
M. Mirzaei;Shaghayegh Sherry Sahebi;Peter Brusilovsky
最近的研究表明，学生在在线教育系统中学习时遵循稳定的行为模式。这些行为模式可以进一步用于将学生分成不同的集群。然而，由于这些组别包括表现优异和表现欠佳的学生，行为模式与学生表现之间的关系仍有待澄清艾德。在这项工作中，我们研究了学生的学习行为和他们的表现之间的关系，在一个自组织的在线学习系统，允许他们自由地练习各种问题和工作的例子。我们将每个学生的行为表示为高支持度序列微模式的向量。然后，我们发现在每个组中的流行的行为模式和跨组使用歧视性非负矩阵分解的共享模式。我们的实验表明，我们可以成功地检测出学生行为中的常见和特殊模式，这些模式可以进一步解释为学生学习行为的特质模式和表现模式。
Recent studies have shown that students follow stable behavioral patterns while learning in online educational systems. These behavioral patterns can further be used to group the students into different clusters. However, as these clusters include both high-and low-performance students, the relation between the behavioral patterns and student performance is yet to be clarified. In this work, we study the relationship be-tween students’ learning behaviors and their performance, in a self-organized online learning system that allows them to freely practice with various problems and worked examples. We represent each student’s behavior as a vector of high-support sequential micro-patterns. Then, we discover both the prevalent behavioral patterns in each group and the shared patterns across groups using discriminative non-negative matrix factorization. Our experiments show that we can successfully detect such common and specific patterns in students’ behavior that can be further interpreted into student learning behavior trait patterns and performance patterns.