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An advantage of Large Language Models (LLMs) is their contextualization capability – providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We present a design and evaluation of a proof-of-concept LLM application to offer students dynamic and contextualized feedback. Specifically, we augment an Online Programming Exercise bot for a college-level Cloud Computing course with ChatGPT, which offers students contextualized reflection triggers during a collaborative query optimization task in database design. We demonstrate that LLMs can be used to generate highly situated reflection triggers that incorporate details of the collaborative discussion happening in context. We discuss in depth the exploration of the design space of the triggers and their correspondence with the learning objectives as well as the impact on student learning in a pilot study with 34 students. 

In this study, an AR-based mobile learning application is proposed to assist online civil engineering course learning during the COVID-19 pandemic. A quasi-experiment has been conducted, and feedback from both the teacher and students has been analysed to examine the effectiveness of the proposed approach in terms of learning achievements. The subjects were 46 sophomores who majored in civil engineering in one class taught by one instructor in a southern U.S. state university. The quasi-experimental results showed that the proposed approach could not significantly improve the students’ online learning achievements. However, the feedbacks brought some explanation to this non-significant result. They indicated that students found this mobile AR app to be an interesting, helpful, practical, and effective approach in their online learning that helped them gain more in-depth knowledge than traditional teacher-centred classroom instruction. 

The National Science Foundation (NSF) held a virtual Symposium on PRedicting Emergence of Virulent Entities by Novel Technologies (PREVENT), on February 22 – 23, 2021 as part of its series on Predictive Intelligence for Pandemic Prevention (PIPP). The workshop brought together more than 60 leading experts, representing NSF research directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), Social, Behavioral and Economic Sciences (SBE), and the Office of International Science and Engineering (OISE), to discuss how the global behavior of an infectious entity can emerge from the interactions that begin occurring between components at the molecular level and expand to physiological, environmental, and population scales. The workshop was divided into four sessions, each focusing on one of four different scales: 1) end-toend (or multi-scale) 2) molecular, 3) physiological and environmental, and 4) population and epidemiological. Particular focus was given to identifying challenges and opportunities in each of these domains. The workshop aimed to: • Identify interdisciplinary advances in science, technology, and human behavior to enable prediction and prevention of future pandemics • Begin to build the necessary convergence to be optimally prepared to prevent future pandemics • Establish convergent data commons and cyberinfrastructure for PIPP This workshop report summarizes the plenary presentations, panel discussions, and breakout group sessions that took place at this event. The results presented here are drawn from the viewpoints expressed by the participants and do not necessarily reflect those of the broader pandemic research community. 

For many students, trigonometry is a difficult subject because it requires strong spatial visualization abilities. A team at Jackson State University makes the teaching and learning process easer with a new learning tool for mobile phones developed using augmented reality (AR). The results indicated that AR incorporated learning tool has great potential for learning trigonometry. 

In the bottomonium sector, the hindered magnetic dipole transitions between P-wave states $h_b\left(2P\right)\to {\chi }_{bJ}\left(1P\right)\gamma$, $J=0$, 1, 2, are expected to be severely suppressed according to the relativized quark model, due to the spin flip of the $b$quark. Nevertheless, a recent model following the coupled-channel approach predicts the corresponding branching fractions to be enhanced by orders of magnitude. In this Letter, we report the first search for such transitions. We find no significant signals and set upper limits at 90% confidence level on the corresponding branching fractions: $\mathcal{B}\left[h_b\right(2P)\to \gamma {\chi }_{b0}(1P\left)\right]<2.7\times {10}^{-1}$, $\mathcal{B}\left[h_b\right(2P)\to \gamma {\chi }_{b1}(1P\left)\right]<5.4\times {10}^{-3}$and $\mathcal{B}\left[h_b\right(2P)\to \gamma {\chi }_{b2}(1P\left)\right]<1.3\times {10}^{-2}$. These values help to constrain the parameters of the coupled-channel models. The results are obtained using a $121.4{\mathrm{fb}}^{-1}$data sample taken around $\sqrt{s}=10.860\mathrm{GeV}$with the Belle detector at the KEKB asymmetric-energy $e^{+}{e}^{-}$collider. Published by the American Physical Society2025