Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain
Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain
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发表时间：
2016
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通讯作者：
Shaghayegh Sherry Sahebi;Y. Lin;Peter Brusilovsky
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作者：
Shaghayegh Sherry Sahebi;Y. Lin;Peter Brusilovsky
© 2016国际教育数据挖掘协会。All rights reserved.我们提出了一种新的张量分解方法，反馈驱动张量分解（FDTF），建模学生的学习过程和预测学生的表现。这种方法分解了一个张量，这是建立在学生的尝试序列，同时考虑到测验学生选择工作作为其反馈。FDTF不需要任何先验领域知识，例如学习资源技能、概念图或Q矩阵。所提出的方法显着不同于其他张量因式分解方法，因为它显式地模拟学生的学习进度，同时与学习资源进行交互。我们比较我们的方法与其他国家的最先进的方法在预测学生成绩（PSP）的任务。我们的实验表明，FDTF的性能显着优于基线方法，包括贝叶斯知识跟踪和最先进的张量分解方法。
© 2016 International Educational Data Mining Society. All rights reserved. We propose a novel tensor factorization approach, Feedback-Driven Tensor Factorization (FDTF), for modeling student learning process and predicting student performance. This approach decomposes a tensor that is built upon students’ attempt sequence, while considering the quizzes students select to work with as its feedback. FDTF does not require any prior domain knowledge, such as learning resource skills, concept maps, or Q-matrices. The proposed approach differs significantly from other tensor factorization approaches, as it explicitly models the learning progress of students while interacting with the learning resources. We compare our approach to other state-of-the-art approaches in the task of Predicting Student Performance (PSP). Our experiments show that FDTF performs significantly better compared to baseline methods, including Bayesian Knowledge Tracing and a state-of-the-art tensor factorization approach.