期刊
KNOWLEDGE-BASED SYSTEMS
卷 143, 期 -, 页码 102-114出版社
ELSEVIER
DOI: 10.1016/j.knosys.2017.12.011
关键词
E-learning; Knowledge map; Learning scenario; Learning path recommendation
资金
- National Key Research and Development Program of China [2016YFB1000903]
- Innovative Research Group of the National Natural Science Foundation of China [61721002]
- Innovation Research Team of Ministry of Education [IRT_17R86]
- National Science Foundation of China [61472315, 61402392, 61428206, 61532015, 61532004]
- project of China Knowledge Centre for Engineering Science and Technology
- Natural Science Basic Research Plan in Shaanxi Province of China [2016JM6027, 2016JM6080]
- Online Education Research Foundation of MOE Research Center for Online Education [2016YB165, 2016YB169]
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.
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