LOD Based Semantically Enhanced Open Learning Space Raises Engagement for Historical Deep Consideration

Abstract

The purpose of this research is to support learners in self-directed 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 Semantically Enhanced Open Learning Space (SOLS). Learners use the system to build a concept map representing their knowledge. SOLS support 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. 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’ historical considerations and deep historical thinking skills. 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 support can be a viable tool to support learners in open learning space and that the question support has potential to support learners during a long time study.