Capture and visualisation of text understanding through semantic annotations and semantic networks for teaching and learning

Abstract

During various learning activities, teachers and students need to clarify, explore and share their understanding of reading materials. For doing this, they must make explicit the mental representation constructed during the reading process. In this article, we propose an approach combining semantic annotations and semantic networks as formal means for elicitation, structuring, formalisation, analysis and sharing of teachers’ and students’ understanding of textual materials that they are asked to read in learning tasks. In the proposed approach, teachers can create learning tasks, in which students are asked to semantically annotate a text by associating portions of it to resources described in a Knowledge Base (KB) in accordance with a provided ontology. New instances can be created by students or teachers in the KB during the annotation process. We show how semantic networks can be used to visualise extracts of the resulting KB, and to help people organise their comprehension of texts. In addition, teachers can assess student evolution by analysing the semantic networks that each one produces during the reading and annotation process. This approach is implemented by using our annotation tool, integrated with a digital repository and a virtual learning environment. An empirical evaluation of the benefits of the proposed approach in a literature case study confirms that it facilitates information extraction, sharing and analysis, contributing to leverage teaching and learning.

Automatic and intelligent content visualization system based on deep learning and genetic algorithm

“…In this system, when users search a keyword, results are visualized using data extraction, transformation, and clustering methods. In another study, the DLNotes2 tool, which is based on semantic networks, was developed to visualize learning activity results in digital libraries [ 52 ]. In these studies, semantic web, ontologies, natural language processing, statistical methods, neural networks, and deep neural networks have been used to represent text with images.…”