Abstract
In a world ‘flooded’ with data, students in school need adequate tools as Visual Analytics (VA), that easily process mass data, give support in drawing advanced conclusions and help to make informed predictions in relation to societal circumstances. Methods for how the students’ insights may be reformulated and presented in ‘appropriate’ modes are required as well. Therefore, the aim in this study is to analyse elementary school students’ practices of communicating visual discoveries, their insights, as the final stage in the knowledge-building process with an VA-application for interactive data visualization. A design-based intervention study is conducted in one social science classroom to explore modes for students presentation of insights, constructed from the interactive data visualizations. Video captures are used to document 30 students’ multifaceted presentations. The analyses are based on concepts from Pennycook (2018) and Deleuze and Guattari (1987). To account for how different modes interact, when students present their findings, one significant empirical sequence is described in detail. The emerging communicative dimensions (visual-, bodily- and verbal-) are embedded within broad spatial repertoires distributing flexible semiotic assemblages. These assemblages provide an incentive for the possibilities of teachers’ assessments of their students’ knowledge outcomes.
Highlights
New demands are placed on the requirements for how students in school both need to be trained to use appropriate tools in order to develop their skills to interpret/analyse large volumes of data and to be encouraged to complete such problem-solving
Visual analytics (VA) is a technology developed to provide data visualization tools that help people to understand the significance of large volumes of data by placing them in multimodal contexts (Andrienko, 2013)
The VA applications have become valuable to society due to their power to support human understanding of circumstances in society and prevalent in many areas as they contribute to various kinds of problem-solving (Andrienko & Andrienko, 2020)
Summary
New demands are placed on the requirements for how students in school both need to be trained to use appropriate tools in order to develop their skills to interpret/analyse large volumes of data and to be encouraged to complete such problem-solving. If students in school are introduced to these powerful multimodal (artificial intelligence (AI)) technologies, this helps them to collaboratively manage analysis of large amounts of data and develop insights (Stenliden, 2014, 2015). The final assignment for students’ is usually to produce a written text, as ‘proof’ of having achieved knowledge (Åkerfeldt, 2014). This rather ‘static’ mode often seems to narrow the possibilities to transfer and demonstrate knowledge gained with multimodal technology. Students would be better off if they, as a final assignment are encouraged to produce ‘proof’ of achived knowledge in multimodal ways that may promote them better (Baldwin, 2016)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
