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The great need and tendency to apply online classes ask for using new technologies in language teaching. Social Networking (SN) tools, in particular, Mobile-Assisted Language Learning (MALL), open new perspectives in language learning and teaching. The employment of SN in language learning may affect the learners' mental health and emotional safety. Despite the attributions of the Telegram application in learning and the contributions of academic buoyancy (AB), academic emotion regulation (AER), and management of foreign language anxiety (FLA) to English achievement (EA), this field was left unexplored. To this end, the current study attempted to gauge the impact of the Telegram-based instruction on AB, AER, FLA, as well as EA. 79 EFL learners took part in the research and were randomly divided in to control group (CG) and experimental group (EG). The instruction for the CG was through regular online instruction (webinar platforms). The EG received Telegram-based instruction. The results of MANOVA displayed significant differences between the post-tests of CG and EG. The findings illustrated that the Telegram instruction improved the levels of AB, AER, and FLA management, which accelerated EA. The pedagogical implications of the study were discussed and may assist learners, teachers, teacher educators, policymakers, materials developers, as well as curriculum designers.
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http://dx.doi.org/10.1016/j.heliyon.2023.e15830 | DOI Listing |
BMC Med Educ
September 2025
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.
Background: Health professions students may encounter a range of stressors during their clinical education that may impact their quality of life. This study aimed to explore how various health professions students perceive their quality of life and the environment in which they develop their clinical skills.
Methods: An online survey was administered among registered undergraduate students in the physiotherapy, speech-language pathology, nursing, or medical programs.
J Imaging Inform Med
September 2025
Department of Biomedical Engineering, Gachon University, Seongnam-Si 13120, Gyeonggi-Do, Republic of Korea.
To develop and validate a deep-learning-based algorithm for automatic identification of anatomical landmarks and calculating femoral and tibial version angles (FTT angles) on lower-extremity CT scans. In this IRB-approved, retrospective study, lower-extremity CT scans from 270 adult patients (median age, 69 years; female to male ratio, 235:35) were analyzed. CT data were preprocessed using contrast-limited adaptive histogram equalization and RGB superposition to enhance tissue boundary distinction.
View Article and Find Full Text PDFNeotrop Entomol
September 2025
Dept of Entomology, Federal Univ of Viçosa, Viçosa, MG, Brazil.
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A.
View Article and Find Full Text PDFMamm Genome
September 2025
Department of Animal Health and Anatomy, Center for Animal Biotechnology and Gene Therapy, Universitat Autònoma de Barcelona, Travessera Dels Turons, 08193, Cerdanyola del Vallès, Barcelona, Spain.
The mouse remains the principal animal model for investigating human diseases due, among other reasons, to its anatomical similarities to humans. Despite its widespread use, the assumption that mouse anatomy is a fully established field with standardized and universally accepted terminology is misleading. Many phenotypic anatomical annotations do not refer to the authority or origin of the terminology used, while others inappropriately adopt outdated or human-centric nomenclature.
View Article and Find Full Text PDFNat Hum Behav
September 2025
Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Sciences, Zhejiang University, Hangzhou, China.
Understanding how sentences are represented in the human brain, as well as in large language models (LLMs), poses a substantial challenge for cognitive science. Here we develop a one-shot learning task to investigate whether humans and LLMs encode tree-structured constituents within sentences. Participants (total N = 372, native Chinese or English speakers, and bilingual in Chinese and English) and LLMs (for example, ChatGPT) were asked to infer which words should be deleted from a sentence.
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