Learning Sequences: An Efficient Data Structure for Learning Spaces
Learning Sequences: An Efficient Data Structure for Learning Spaces
复制标题
学习序列：学习空间的高效数据结构
DOI：
10.1007/978-3-642-35329-1_13
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发表时间：
2013
影响因子：
3.9
通讯作者：
D. Eppstein
中科院分区：
文献类型：
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作者：
D. Eppstein
学习空间构成了组合理论的基础，该理论是成功部署在计算机评估和学习系统（例如Aleks）中的人类学习者知识状态的基础（Falmagne和Doignon，2011年）。然而，直到最近，这些系统的计算效率及其准确评估用户知识的能力都受到理论和实践之间的不匹配而阻碍：他们使用了基于部分有序集和准集的简化版本的学习空间理论。 - 内部空间，导致计算效率低下和评估不准确。在本章中，我们介绍了算法和数据结构方面的最新发展，这些算法和数据结构使学习系统能够使用完整的学习空间理论。我们的方法基于学习序列，一系列步骤序列，从没有知识开始的学生可以学习空间中的所有概念。我们展示了如何通过学习序列来定义学习空间以及如何使用学习序列有效执行评估算法的步骤
Learning spaces form the basis of a combinatorial theory of the possible states of knowledge of a human learner that has been successfully deployed in computerized assessment and learning systems such as ALEKS (Falmagne and Doignon, 2011). Until recently, however, both the computational efficiency of these systems and their ability to accurately assess the knowledge of their users have been hampered by a mismatch between theory and practice: they used a simplified version of learning space theory based on partially ordered sets and quasi-ordinal spaces, leading both to computational inefficiencies and to inaccurate assessments. In this chapter we present more recent developments in algorithms and data structures that have allowed learning systems to use the full theory of learning spaces. Our methods are based on learning sequences, sequences of steps through which a student, starting with no knowledge, could learn all the concepts in the space. We show how to define learning spaces by their learning sequences and how to use learning sequences to efficiently perform the steps of an assessment algorithm