In my diploma thesis I am dealing with the problematics of reading comprehension by children with a different mother tongue (immigrant children) at the elementary school. I point out the importance of visualization in the learning process and various forms of graphic text visualizations which help the children to comprehend and adopt the syllabus. I specifically concentrate on texts in the subjects Science and History which are demanding for immigrant children, mainly because of a high number of special words and information. In the theoretical part I give brief information about the education of immigrant children at Czech schools, their problems in the Czech language, reading comprehension, the visualization and the possibilities of graphic visualizations. In the practical part I decided to use a qualitative survey, namely the method of the case study. I use the method of participant observation and in-depth interviews with the teacher and the student who I create for the special worksheets based on principle of graphic visualization and I monitor and examine their progress in adopting the vocabulary and discussed syllabus. The aim of my thesis is to map the forms of text visualizations, to use them appropriately when creating the working sheets, to incorporate them into the training of reading...

The amount of available text data has increased rapidly in the latest years, making it difficult for an everyday user to find relevant information. To solve this, NLP and visualisation methods have been developed for extracting valuable information from text and presenting it to the user. The aim of this project is to implement a proof-of-concept visualisation prototype for exploring a large amount of Swedish news articles with related metadata and investigate the temporal and relational aspects of the data. The project was divided into three major parts. In the first part, sketches of the visualisation were designed and evaluated through user tests. The second part consisted of designing and implementing a NLP pipeline, using BERTopic, where both Dynamic Topic Modeling (DTM) and Hierarchical Topic Modeling (HTM) were used. Some parameters of the pipeline were evaluated using evaluation metrics and through visual inspection, for instance a Swedish sentence transformer. The final part consisted of implementing and evaluating the visualisation prototype. The project resulted in a web-based visualisation, presenting the NLP results, with two different views: a top 10 topics view and a hierarchical view containing all topics. The prototype has various features, e.g., clicking and hovering for details-on-demand and options for changing and altering the view. The prototype was then evaluated through an internal case study and user tests. For the user tests, there were two groups of participants: people working in the journalism field and people working closely to the NLP field. Both groups thought there was more value in viewing the top 10 topics view than the hierarchical view. Furthermore, the quality of the top 10 topics view was considered higher overall compared to the hierarchical view. In the end, the result of this project is a proof-of-concept visualisation prototype presenting topics of Swedish news articles, over time and in relation to each other. A few possible improvement possibilities include improving the hierarchical relations between the topics and the run time of the topic model and prototype. Also, the prototype may be further improved with additional features, e.g., real-time data, a map, the full text of the articles and a search function. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>