An experimental study was designed to analyze the effect of school-based training in self-regulation learning strategies on academic performance (Mathematics, Sciences, Language, and English). Class-level variables (i.e., gender, the teacher's teaching experience, class size) were considered and the effects of the intervention were measured at the end of the intervention and 3 months later. A sample of 761 students from 3rd and 4th grades (356 in the control condition and 405 in the experimental condition), from 14 schools, participated in the study. Data were analyzed using three-level analysis with within-student measurements at level 1, between-students within-classes at level 2, and between-classes at level 3. Data showed a positive effect of the intervention on student performance, both at post-test ( = 0.25) and at follow-up ( = 0.33) considering the four school subjects together. However, the effect was significant just at follow-up when subjects were considered separately. Student performance was significantly related to the students' variables (i.e., gender, level of reading comprehension) and the context (teacher gender and class size). Finally, students' gender and level of reading comprehension, as well as the teacher's gender, were found to moderate the effect of the intervention on students' academic performance. Two conclusions were highlighted: first, data emphasize the importance of considering time while conducting intervention studies. Second, more teaching experience does not necessarily translate into improvements in the quality of students' instruction.
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http://dx.doi.org/10.3389/fpsyg.2022.889201 | DOI Listing |
Anat Sci Educ
January 2025
Tissue Engineering Group, Department of Histology, Medical School, University of Granada, Granada, Spain.
The recent coronavirus disease (COVID-19) forced pre-university professionals to modify the educational system. This work aimed to determine the effects of pandemic situation on students' access to medical studies by comparing the performance of medical students. We evaluated the performance of students enrolled in a subject taught in the first semester of the medical curriculum in two pre-pandemic academic years (PRE), two post-pandemic years (POST), and an intermediate year (INT) using the results of a final multiple-choice exam.
View Article and Find Full Text PDFJ Med Radiat Sci
January 2025
Royal Adelaide Hospital, Adelaide, South Australia, Australia.
This letter critically evaluates the conclusions drawn by Li et al. (https://doi.org/10.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Research and Development Center of Optoelectronic Hybrid IC, Guangdong Greater Bay Area Institute of Integrated Circuit and System, Guangzhou 510535, China.
Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, 5000-801 Vila Real, Portugal.
Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management.
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