In many countries, early mathematical learning takes place in informal and play-based situations. To support children's mathematical learning, the interactions that occur in the daily contact between the early childhood (EC) teacher and the child in kindergarten play an important role. In these interactions, the feedback provided by the EC teacher is considered to have effects on learning. However, how EC teachers actually give specific or non-specific feedback in everyday activities and play situations with a potential for mathematical learning (natural mathematical learning situations) has been little studied so far. To comprehensively characterize the EC teacher's feedback in natural mathematical learning situations, the current study developed a detailed category system based on categories from previous feedback studies, conducted under various conditions and with different objectives. To verify our category system, we coded mathematical teacher-child interactions (N = 162). The coding provided us with evidence that the category system allows to capture the given feedback in natural mathematical learning situations reliably and in detail. The category system can be useful for further research examining the effects of naturally given feedback on children's mathematical learning and, in the long run, for training teachers in the use of potentially supportive feedback in natural learning situations.
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http://dx.doi.org/10.1016/j.actpsy.2024.104175 | DOI Listing |
Updates Surg
January 2025
Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for prediction and classification problems.
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January 2025
Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China.
Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available.
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January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
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January 2025
Curtin School of Allied Health, Curtin University, Perth 6102, Australia.
In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when further assessment is necessary.
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January 2025
School of Automation and Electrical Engineering, Beihang University, Beijing 100191, China.
Since the field of autonomous vehicles is developing quickly, it is becoming increasingly crucial for them to safely and effectively navigate their surroundings to avoid collisions. The primary collision avoidance algorithms currently employed by self-driving cars are examined in this thorough survey. It looks into several methods, such as sensor-based methods for precise obstacle identification, sophisticated path-planning algorithms that guarantee cars follow dependable and safe paths, and decision-making systems that allow for adaptable reactions to a range of driving situations.
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