Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization
Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization
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DOI：
10.1109/jstsp.2017.2705581
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发表时间：
2017-08-01
影响因子：
7.5
通讯作者：
Karypis, George
中科院分区：
文献类型：
--
作者：
Almutairi, Faisal M.;Sidiropoulos, Nicholas D.;Karypis, George
学生留存率和按时毕业是高等教育中持续存在的挑战。随着学习数据的收集和可用性迅速增加以及相关分析的发展，可以准确地监测学生的表现，并可能提前进行预测，从而使预警和学位规划“专家系统”能够为辅导员、导师和教育工作者提供规范的决策支持。先前在教育数据挖掘方面的工作已经探索了用于成绩预测的矩阵分解技术，尽管没有考虑到情境信息。时间信息应该是有信息量的，因为它区分了不同的课程设置，并间接地反映了学生的经历。为了利用时间和/或其他类型的情境，我们在协同过滤（CF）框架下开发了三种方法。其中两种提出的方法基于具有共享潜在矩阵因子的耦合矩阵分解。第三种方法利用张量分解对成绩及其情境进行建模，而不像协同过滤文献中常见的那样为每个情境维度引入一个新模式。如果需要，获得的潜在因子可用于预测成绩和情境。我们使用从明尼苏达大学获得的成绩数据对这些方法进行评估。实验结果表明，即使使用简单的方法也有可能进行相当不错的预测，但很难做到非常准确的预测。更先进的方法可以提高预测准确性，但对于所考虑的特定数据集来说，只能提高到一定程度。
Student retention and timely graduation are enduring challenges in higher education. With the rapidly expanding collection and availability of learning data and related analytics, student performance can be accurately monitored, and possibly predicted ahead of time, thus, enabling early warning and degree planning "expert systems" to provide disciplined decision support to counselors, advisors, and educators. Previous work in educational data mining has explored matrix factorization techniques for grade prediction, albeit without taking contextual information into account. Temporal information should be informative as it distinguishes between the different class offerings and indirectly captures student experience as well. To exploit temporal and/or other kinds of context, we develop three approaches under the framework of collaborative filtering (CF). Two of the proposed approaches build upon coupled matrix factorization with a shared latent matrix factor. The third utilizes tensor factorization to model grades and their context, without introducing a new mode per context dimension as is common in the CF literature. The latent factors obtained can be used to predict grades and context, if desired. We evaluate these approaches on grade data obtained from the University of Minnesota. Experimental results show that fairly good prediction is possible even with simple approaches, but very accurate prediction is hard. The more advanced approaches can increase prediction accuracy, but only up to a point for the particular dataset considered.