IMAGE-TEXT RELATION INTERPRETATION: TEACHERS’ VISUAL-VERBAL COMPETENCE IN TEACHING TEXTS

Multimodal literacy instruction is such a new shift of the literacy in which the construction of knowledge is led to be more socially and contextually bounded. Due to the urgency that teachers have to own multimodal competencies, this study aimed to investigate EFL teachers’ competence in interpreting visual-verbal relations to teach multimodal texts. To collect the data, an online test through the Google form platform was distributed. As many as 43 responses were collected from junior and senior high school teachers in one of the cities in Indonesia. A semi-structured interview was also conducted with six purposive participants. The data in this research were then analysed based on Royce’s criteria of image-text relation. The analysis found that the teachers only partially possessed multimodal competencies. It means that that they had used images to help them teach the texts but had insufficient knowledge on how to utilize the images as meaning-making sources. Then, based on the finding, it is suggested that the teachers should improve their competences in interpreting multimodal meanings in texts, so images are used not only for making learning materials interesting but also for making more meanings from the texts.

Becoming.a(Thing): An Artists’ Perspective on High-Performance Computing

This article summarizes the process and outcome of the Future Emerging Art and Technology residency during which new media artists Špela Petrič and Miha Turšič undertook the challenge of understanding and manifesting the artistic potential of high-performance computing (HPC). As a result of the collaboration with FET HPC the artists developed a concept liberated from complex computational technicity to underscore the (un)intentional construction of meaning by algorithmic agencies. The performance presents a congress of actors sensing, interrogating and interrupting each other, thereby producing an excess of relation, interpretation and translation. The heterogeneous congress performs an expulsion of imposed (anthropogenic) meaning, substituted by authentic, autogenic sense and non-sense.

Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering

Valuable knowledge has been distributed in heterogeneous formats on many different Web sites and other sources over the Internet. However, finding the needed information is a complex task since there is a lack of semantic relations and organization between them. This chapter presents a problem-solving map framework for extracting and integrating knowledge from unstructured documents on the Internet by exploiting the semantic links between problems, methods for solving them and the people who could solve them. This challenging area of research needs both complex natural language processing, including deep semantic relation interpretation, and the participation of end-users for annotating the answers scattered on the Web. The framework is evaluated by generating problem solving maps for rice and human diseases.