Big Data as a Technology-to-think-with for Scientific Literacy

This research aimed to identify indications of scientific literacy resulting from a didactic and investigative interaction with Google Trends Big Data software by first-year students from a high-school in Novo Hamburgo, Southern Brazil. Both teaching strategies and research interpretations lie on four theoretical backgrounds. Firstly, Bunge's epistemology, which provides a thorough characterization of Science that was central to our study. Secondly, the conceptual framework of scientific literacy of Fives et al. that makes our teaching focus precise and concise, as well as supports one of our methodological tool: the SLA (scientific literacy assessment). Thirdly, the "crowdledge" construct from dos Santos, which gives meaning to our study when as it makes the development of scientific literacy itself versatile for paying attention on sociotechnological and epistemological contemporary phenomena. Finally, the learning principles from Papert's Constructionism inspired our educational activities. Our educational actions consisted of students, divided into two classes, investigating phenomena chose by them. A triangulation process to integrate quantitative and qualitative methods on the assessments results was done. The experimental design consisted in post-tests only and the experimental variable was the way of access to the world. The experimental group interacted with the world using analyses of temporal and regional plots of interest of terms or topics searched on Google. The control class did 'placebo' interactions with the world through on-site observations of bryophytes, fungus or whatever in the schoolyard. As general results of our research, a constructionist environment based on Big Data analysis showed itself as a richer strategy to develop scientific literacy, compared to a free schoolyard exploration.Comment: 23 pages, 2 figures, 8 table

Enhancing Physics Learning with ChatGPT, Bing Chat, and Bard as Agents-to-Think-With: A Comparative Case Study

The rise of AI has brought remarkable advancements in education, with AI models demonstrating their ability to analyse and provide instructive solutions to complex problems. This study compared and analysed the responses of four Generative AI-powered chatbots (GenAIbots) - ChatGPT-3.5, ChatGPT-4, Bing Chat, and Bard - within the constructivist theoretical framework. Using a single-case study methodology, interaction logs between the GenAIbots and a simulated student in Physics learning scenarios were analysed. The GenAIbots were presented with conceptually dense Physics problems to promote deep understanding. The qualitative analysis focused on tutor traits such as subject-matter knowledge, empathy, assessment emphasis, facilitation skills, and comprehension of the learning process. Findings showed that all GenAIbots functioned as agents-to-think-with, fostering critical thinking, problem-solving, and subject-matter knowledge. ChatGPT-4 stood out for demonstrating empathy and a deep understanding of the learning process. However, inconsistencies and shortcomings were observed, highlighting the need for human intervention in AI-assisted learning. In conclusion, while GenAIbots have limitations, their potential as agents-to-think-with in Physics education offers promising prospects for revolutionising instruction