SOLS: An LOD based semantically enhanced open learning space supporting self-directed learning of history

Abstract

The purpose of this research is to support learners in selfdirected learning on the Internet using automatically generated support using the current state of the semantic web. The main issue of creating meaningful content-dependent questions automatically is that it requires the machine to understand the concepts in the learning domain. The originality of this work is that it uses Linked Open Data (LOD) to enable meaningful content-dependent support in open learning space. Learners are supported by a learning environment, the Semantic Open Learning Space (SOLS). Learners use the system to build a concept map representing their knowledge. SOLS supports learners following the principle of inquiry-based learning. Learners that request help are provided with automatically generated questions that give them learning objectives. To verify whether the current system can support learners with fully automatically generated support, we evaluated the system with three objectives: judge whether the LOD based support was feasible and useful, whether the question support improved the development of historical considerations in the learners' mind and whether the engagement of learners was improved by the question support. The results showed that LOD based support was feasible. Learners felt that the support provided was useful and helped them learn. The question support succeeded in improving the development of learners' deep historical considerations. In addition, the engagement and interest in history of learners was improved by the questions. The results are meaningful because they show that LOD based question support can be a viable tool to support self-directed learning in open learning space.