A multi-constraint learning path recommendation algorithm based on knowledge map
A multi-constraint learning path recommendation algorithm based on knowledge map
复制标题
一种基于知识图谱的多约束学习路径推荐算法
DOI：
10.1016/j.knosys.2017.12.011
复制
发表时间：
2018-03-01
影响因子：
8.8
通讯作者：
Zheng, Qinghua
中科院分区：
文献类型：
--
作者：
Zhu, Haiping;Tian, Feng;Zheng, Qinghua
网络学习者在面对大量学习资源时，尤其是在各种学习场景下，需要平衡有限的学习时间和多个学习目标时，很难做出如何学习的决策。针对这一问题，提出了一种基于知识地图的多约束学习路径推荐算法。本文的主要贡献如下。首先，通过基于统计分析的方法，验证了网络学习者在四种不同的学习场景（初始学习、平时复习、考前学习和考前复习）下不同的学习路径偏好的两个假设。其次，根据四类学习场景的学习行为特征，提出了一种多约束学习路径推荐模型，模型中的变量及其权重系数考虑了不同学习场景下学习者的学习路径偏好，以及学习资源组织和时间碎片化。第三，基于所提出的模型和知识地图，设计并实现了一种多约束学习路径推荐算法。最后，通过对110多名网络学习者的问卷调查，验证了该算法的有效性，并显示了学习者自组织学习路径与推荐学习路径的相似性。(C)2017爱思唯尔B.V.保留所有权利。
It is difficult for e-learners to make decisions on how to learn when they are facing with a large amount of learning resources, especially when they have to balance available limited learning time and multiple learning objectives in various learning scenarios. This research presented in this paper addresses this challenge by proposing a new multi-constraint learning path recommendation algorithm based on knowledge map. The main contributions of the paper are as follows. Firstly, two hypotheses on e-learners' different learning path preferences for four different learning scenarios (initial learning, usual review, pre-exam learning and pre-exam review) are verified through questionnaire-based statistical analysis. Secondly, according to learning behavior characteristics of four types of the learning scenarios, a multi constraint learning path recommendation model is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time. Thirdly, based on the proposed model and knowledge map, the design and implementation of a multi-constraint learning path recommendation algorithm is described. Finally, it is shown that the questionnaire results from over 110 e-learners verify the effectiveness of the proposed algorithm and show the similarity between the learners' self-organized learning paths and the recommended learning paths. (C) 2017 Elsevier B.V. All rights reserved.