Can We Use LOD to Generate Meaningful Questions for History Learning

This research aims to clarify the potential of LOD (Linked Open Data) as a learning resource. In this paper, we describe a method that uses LOD to generate content-dependent questions in the history domain. Our method combines history domain ontology to LOD to create questions about any historical topic. To prove whether the generated questions have the potential to support learning, a human expert conducted an evaluation comparing our automatically generated questions with questions generated manually. The results of the evaluation showed that the generated questions could cover most (87%) of the questions supporting knowledge acquisition generated by humans. In addition, we confirmed that the system can generate questions that enhance history thinking of the same quality as human generated questions. The current state of linked open data (LOD) provides a large amount of content. It is possible to access semantic information about many domains. In this paper, we aim to verify whether it is possible to generate meaningful content-dependent questions that support history learning in an open learning space by using the current state of LOD sources. Questions from the teacher are an important and integral part of the learning to deepen learners’ understanding [Roth 96]. More specifically, in the history domain, asking questions to learners encourages them to form an opinion and reinforce their understanding [Husbands 96]. Learners also naturally ask questions to themselves during their learning. However, they cannot always generate good questions by themselves [Otero 09]. Because the quality of the learning is dependent on the quality of the questions [Bransford 99], asking good questions is important for performing satisfying learning. Learners are required to generate good questions to perform good quality of learning. This is one of the difficulties of learners performing their learning by themselves. Our approach to solve this problem is to support learners with automatically generate meaningful questions depending on the contents of the documents studied by each learner. Our question generation method adopts a semantic approach that uses the LOD and ontologies to create content-dependent questions. Our function is part of a novel learning environment [Jouault 13] that aims to provide meaningful support in history learning about any historical topic. The first issue to be clarified to build the question generation function, which is applicable to an open learning space, is (a) how we build scalable and reliable knowledge resource based on LOD and (b) how we build history dependent question ontology to generate content-dependent questions. The second issue to be clarified is whether the quality of the questions generated by the system is sufficient to support history learning. We must evaluate the function before using it because, as we mentioned, the quality of the learning is dependent on the quality of the question generated by the system.