| 论文简介 |
It is difficult for e-learners to make decision on how to learn when they are facing with large amount of learning resources, especially when they need to balance available limited learning time and multiple learning objectives in various learning scenarios. This paper attempts to address 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 the e-learners’ need of different learning paths 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, seven kinds of learning path constraint factors are proposed to determine different kinds of learning paths, such as shortest learning path, critical learning path and easy learning path. Thirdly, the proposed multi- constraint learning path recommendation algorithm based on the knowledge map is implemented and it combines the domain knowledge structure and cognitive structure of the learners to meet their need on different learning paths for the different learning scenarios. Finally, the results calculated from questionnaires verifies the similarity between the learners’ self-organized learning path and the recommended learning path. |
(创新港)
