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Computer‐based scaffoldings influence students\u27 metacognitive monitoring and problem‐solving efficiency in an intelligent tutoring system
No full textBackground Computer‐based scaffolding has been intensively used to facilitate students\u27 self‐regulated learning (SRL). However, most previous studies investigated how computer‐based scaffoldings affected the cognitive aspect of SRL, such as knowledge gains and understanding levels. In contrast, more evidence is needed to examine the effects of scaffolding on the metacognitive dimension and efficiency outcome of SRL. Objectives This study aims to examine the role of computer‐based scaffolding in students\u27 metacognitive monitoring and problem‐solving efficiency. Methods Seventy‐two medical students completed two clinical reasoning tasks in BioWorld, an intelligent tutoring system (ITS) designed for promoting medical students\u27 diagnostic expertise. During solving the tasks, students were asked to report their confidence judgements about proposed diagnoses. Computer trace data were used to identify task completion time (CT) and students\u27 use of three scaffolding types, that is, conceptual, strategic, and metacognitive. Then we calculated students\u27 metacognitive monitoring accuracy (i.e., calibration) and problem‐solving efficiency. Results and Conclusions One‐sample t ‐test demonstrated that students inaccurately monitored their learning processes and were overconfident in both tasks. Linear mixed‐effects models (LMMs) indicated that the intensive use of metacognitive scaffolding positively predicted students\u27 metacognitive monitoring accuracy. Moreover, strategic scaffolding was negatively related to problem‐solving efficiency, whereas metacognitive scaffolding positively influenced problem‐solving efficiency. Takeaways This study shows the importance of metacognitive scaffolding in improving the accuracy of metacognitive monitoring and problem‐solving efficiency. Findings from this study provide new insights for instructors and ITS developers to optimise the design of scaffoldings. , Lay Description What is already known about this topic? Computer‐based scaffolding can facilitate students\u27 knowledge gains and academic performance during self‐regulated learning (SRL). The effects of computer‐based scaffolding on the metacognitive dimension and efficiency outcome of SRL are still in a nascent phase. What this paper adds? This paper explores how different types of computer‐based scaffolding affect students\u27 metacognitive monitoring and problem‐solving efficiency. Metacognitive scaffolding improved the accuracy of medical students\u27 confidence judgements. Metacognitive scaffolding promoted medical students\u27 diagnostic problem‐solving efficiency. Strategic scaffolding had a negative effect on problem‐solving efficiency. Implications for practice Instructors could provide more metacognitive scaffoldings to facilitate students\u27 metacognitive monitoring accuracy. The designers of intelligent tutoring systems and instructors should consider the costs associated with using scaffoldings
Exploring the Role of Artificial Intelligence in Facilitating Assessment of Writing Performance in Second Language Learning
No full textThis study examined the robustness and efficiency of four large language models (LLMs), GPT-4, GPT-3.5, iFLYTEK and Baidu Cloud, in assessing the writing accuracy of the Chinese language. Writing samples were collected from students in an online high school Chinese language learning program in the US. The official APIs of the LLMs were utilized to conduct analyses at both the T-unit and sentence levels. Performance metrics were employed to evaluate the LLMs’ performance. The LLM results were compared to human rating results. Content analysis was conducted to categorize error types and highlight the discrepancies between human and LLM ratings. Additionally, the efficiency of each model was evaluated. The results indicate that GPT models and iFLYTEK achieved similar accuracy scores, with GPT-4 excelling in precision. These findings provide insights into the potential of LLMs in supporting the assessment of writing accuracy for language learners.</jats:p
Molecular architecture of the Gαi-bound TRPC5 ion channel
No full textAbstractG-protein coupled receptors (GPCRs) and ion channels serve as key molecular switches through which extracellular stimuli are transformed into intracellular effects, and it has long been postulated that ion channels are direct effector molecules of the alpha subunit of G-proteins (Gα). However, no complete structural evidence supporting the direct interaction between Gα and ion channels is available. Here, we present the cryo-electron microscopy structures of the human transient receptor potential canonical 5 (TRPC5)-Gαi3complexes with a 4:4 stoichiometry in lipid nanodiscs. Remarkably, Gαi3binds to the ankyrin repeat edge of TRPC5 ~ 50 Å away from the cell membrane. Electrophysiological analysis shows that Gαi3increases the sensitivity of TRPC5 to phosphatidylinositol 4,5-bisphosphate (PIP2), thereby rendering TRPC5 more easily opened in the cell membrane, where the concentration of PIP2is physiologically regulated. Our results demonstrate that ion channels are one of the direct effector molecules of Gα proteins triggered by GPCR activation–providing a structural framework for unraveling the crosstalk between two major classes of transmembrane proteins: GPCRs and ion channels.</jats:p
Anisotropic Etching of InGaN Thin Films with Photoelectrochemical Etching to Form Quantum Dots
No full textTraditional methods for synthesizing InGaN quantum dots (QDs), such as the Stranski-Krastanov growth, often result in QD ensembles with low density and non-uniform size distribution. To overcome these challenges, forming QDs using photoelectrochemical (PEC) etching with coherent light has been developed. Anisotropic etching of InGaN thin films is demonstrated here with PEC etching. InGaN films are etched in dilute H2SO4 and exposed to a pulsed 445 nm laser with a 100 mW/cm2 average power density. Two potentials (0.4 V or 0.9 V) measured with respect to an AgClAg reference electrode are applied during PEC etching, resulting in different QDs. Atomic force microscope images show that while the QD density and sizes are similar for both applied potentials, the heights are more uniform and match the initial InGaN thickness at the lower applied potential. Schrodinger-Poisson simulations show that polarization-induced fields in the thin InGaN layer prevent positively charged carriers (holes) from arriving at the c-plane surface. These fields are mitigated in the less polar planes resulting in high etch selectivity for the different planes. The higher applied potential overcomes the polarization fields and breaks the anisotropic etching.</jats:p
Point process microstructural model of metallic thin films with implications for coarsening
No full textAbstractWe develop a thin-film microstructural model that represents structural markers (i.e., triple junctions in the two-dimensional projections of the structure of films with columnar grains) in terms of a stochastic, marked point process and the microstructure itself in terms of a grain-boundary network. The advantage of this representation is that it is conveniently applicable to the characterization of microstructures obtained from crystal orientation mapping, leading to a picture of an ensemble of interacting triple junctions, while providing results that inform grain-growth models with experimental data. More specifically, calculated quantities such as pair, partial pair and mark correlation functions, along with the microstructural mutual information (entropy), highlight effective triple junction interactions that dictate microstructural evolution. To validate this approach, we characterize microstructures from Al thin films via crystal orientation mapping and formulate an approach, akin to classical density functional theory, to describe grain growth that embodies triple-junction interactions.</jats:p
Inner skin effects on non-Hermitian topological fractals
No full textAbstractNon-Hermitian (NH) crystals, quasicrystals, and amorphous network display an accumulation of a macroscopic number of states near one of its specific interfaces with vacuum, such as edge, surface, hinge, or corner. This phenomenon is known as the NH skin effect, which can only be observed with open boundary condition. In this regard self-similar fractals, manifesting inner boundaries in the interior of the system, harbor a novel phenomenon, the inner skin effect (ISE). Then the NH skin effect appears at the inner boundaries of the fractal lattice with periodic boundary condition. We showcase this observation by implementing prominent models for NH insulators and superconductors on representative planar Sierpinski carpet fractal lattices. They accommodate both first-order and second-order ISEs at inner edges and corners, respectively, for charged as well as neutral Majorana fermions. Furthermore, over extended parameter regimes ISEs are tied with nontrivial bulk topological invariants, yielding intrinsic ISEs. With the recent success in engineering NH topological phases on highly tunable metamaterial platforms, such as photonic and phononic lattices, as well as topolectric circuits, the proposed ISEs can be observed experimentally at least on fractal metamaterials with periodic boundary condition.</jats:p
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
No full textNeural operators (NOs) have emerged as effective tools for modeling complex
physical systems in scientific machine learning. In NOs, a central
characteristic is to learn the governing physical laws directly from data. In
contrast to other machine learning applications, partial knowledge is often
known a priori about the physical system at hand whereby quantities such as
mass, energy and momentum are exactly conserved. Currently, NOs have to learn
these conservation laws from data and can only approximately satisfy them due
to finite training data and random noise. In this work, we introduce
conservation law-encoded neural operators (clawNOs), a suite of NOs that endow
inference with automatic satisfaction of such conservation laws. ClawNOs are
built with a divergence-free prediction of the solution field, with which the
continuity equation is automatically guaranteed. As a consequence, clawNOs are
compliant with the most fundamental and ubiquitous conservation laws essential
for correct physical consistency. As demonstrations, we consider a wide variety
of scientific applications ranging from constitutive modeling of material
deformation, incompressible fluid dynamics, to atmospheric simulation. ClawNOs
significantly outperform the state-of-the-art NOs in learning efficacy,
especially in small-data regimes
Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S.
No full textThe characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain‐specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made adjustments to the methods to reduce their complexity. We used the beta‐transformed linear pool, the finite beta mixture model, and their equal weight adaptations to produce ensemble forecasts retrospectively for the 2016/2017, 2017/2018, and 2018/2019 influenza seasons in the U.S. We compared their performance to methods that were used in the FluSight challenge to produce the FluSight Network ensemble, namely the equally weighted linear pool and the linear pool. Ensemble forecasts produced from methods with a beta transformation were shown to outperform those from the equally weighted linear pool and the linear pool for all week‐ahead targets across in the test seasons based on average log scores. We observed improvements in overall accuracy despite the beta‐transformed linear pool or beta mixture methods\u27 modest under‐prediction across all targets and seasons. Combination techniques that explicitly adjust for known calibration issues in linear pooling should be considered to improve probabilistic scores in outbreak settings
Functionally Distinct Circuits Are Linked by Heterocellular Electrical Synapses in the Thalamic Reticular Nucleus
No full textThe thalamic reticular nucleus (TRN) inhibits sensory thalamocortical relay neurons and is a key regulator of sensory attention as well as sleep and wake states. Recent developments have identified two distinct genetic subtypes of TRN neurons, calbindin-expressing (CB) and somatostatin-expressing (SOM) neurons. These subtypes differ in localization within the TRN, electrophysiological properties, and importantly, targeting of thalamocortical relay channels. CB neurons send inhibition to and receive excitation from first-order thalamic relay nuclei, while SOM neurons send inhibition to and receive excitation from higher-order thalamic areas. These differences create distinct channels of information flow. It is unknown whether TRN neurons form electrical synapses between SOM and CB neurons and consequently bridge first-order and higher-order thalamic channels. Here, we use GFP reporter mice to label and record from CB-expressing and SOM-expressing TRN neurons. We confirm that GFP expression properly differentiates TRN subtypes based on electrophysiological differences, and we identified electrical synapses between pairs of neurons with and without common GFP expression for both CB and SOM types. That is, electrical synapses link both within and across subtypes of neurons in the TRN, forming either homocellular or heterocellular synapses. Therefore, we conclude that electrical synapses within the TRN provide a substrate for functionally linking thalamocortical first-order and higher-order channels within the TRN.</jats:p