Song Wei, Ye Wei, Fournier-Viger Philippe
School of Information Science and Technology, North China University of Technology, Beijing, 100144 China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060 China.
Appl Intell (Dordr). 2022;52(14):16458-16474. doi: 10.1007/s10489-021-03122-7. Epub 2022 Mar 23.
Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed: the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.
在线学习在教育中发挥着越来越重要的作用。大规模开放在线课程（MOOC）平台是在线学习中最重要的工具之一，并记录了大量学习者的历史学习数据。为了提升学习体验，一种很有前景的方法是应用序列模式挖掘（SPM）来从这些数据中发现有用的知识。本文遵循该研究方法，提出了在MOOC注册数据中挖掘具有灵活约束的序列模式（SP）。提出了三个约束条件：长度约束、离散性约束和有效性约束。它们分别用于描述注册序列长度、注册日期差异和注册时刻的影响。为了提高挖掘效率，将这三个约束条件纳入支持度中，支持度是SPM中最典型的参数，从而形成一个名为具有灵活约束的支持度（SFC）的新参数。证明了SFC满足向下封闭性，并提出了两种算法来发现具有灵活约束的SP。它们以广度优先和深度优先的方式遍历搜索空间。实验结果表明，所提出的算法有效地减少了模式数量，性能与经典SPM算法相当。
