1,417 research outputs found
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
Wrong sign and symmetric limits and non-decoupling in 2HDMs
We analyse the possibility that, in two Higgs doublet models, one or more of
the Higgs couplings to fermions or to gauge bosons change sign, relative to the
respective Higgs Standard Model couplings. Possible sign changes in the
coupling of a neutral scalar to charged ones are also discussed. These
\textit{wrong signs} can have important physical consequences, manifesting
themselves in Higgs production via gluon fusion or Higgs decay into two gluons
or into two photons. We consider all possible wrong sign scenarios, and also
the \textit{symmetric limit}, in all possible Yukawa implementations of the two
Higgs doublet model, in two different possibilities: the observed Higgs boson
is the lightest CP-even scalar, or the heaviest one. We also analyse thoroughly
the impact of the currently available LHC data on such scenarios. With all 8
TeV data analysed, all wrong sign scenarios are allowed in all Yukawa types,
even at the 1 level. However, we will show that B-physics constraints
are crucial in excluding the possibility of wrong sign scenarios in the case
where is below 1. We will also discuss the future prospects for
probing the wrong sign scenarios at the next LHC run. Finally we will present a
scenario where the alignment limit could be excluded due to non-decoupling in
the case where the heavy CP-even Higgs is the one discovered at the LHC.Comment: 20 pages, 15 figure
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
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