The purpose of this research includes analyzing the interaction between online English learning motivations, digital readiness, academic engagement, self-regulated English learning attainment, and technology self-efficacy. These interactions were examined based on data gathered from learners in online English courses through the method of structural equation modeling. Analysis found that online English learning motivation has a significant impact on the learners' level of digital readiness and levels of academic engagement, thereby underlining its importance in getting learners ready for meaningful navigation of the digital environments with emphasis on specific academic tasks. Moreover, digital readiness and academic engagement mediate the link between online English learning motivation and online self-regulated learning, thus signifying the role of these variables in achieving translation of motivation into efficient self-regulation. Moreover, online English learning motivation exerts an indirect impact on digital readiness and academic engagement through the moderation of technology self-efficacy; in particular, the high level of self-efficacy strengthens the positive effects of motivation. This basically emphasizes the confidence in technology usage as a fundamental dimension to the effectiveness of online learning. Hence, based on these findings it can be concluded that increasing online English learning motivation, readiness for digital learning environments and academic engagement as well as the technology self-efficacy is important in order to achieve the greatest learning success in the field of online English learning. Although the study has its drawbacks like the use of self-reported data and cross-sectional research design, the study present implications that would be useful to educators and policymakers in the improvement of the online education strategies. The next steps in the research should involve following up the participants over a long period and using both qualitative and quantitative methods to augment these results.
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http://dx.doi.org/10.1016/j.actpsy.2025.104740 | DOI Listing |
JMIR Mhealth Uhealth
January 2025
Department of Learning and Workforce Development, The Netherlands Organisation for Applied Scientific Research, Soesterberg, Netherlands.
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View Article and Find Full Text PDFNeurol Res Pract
January 2025
Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-Universität Würzburg (JMU), Haus D7, Josef-Schneider-Straße 2, 97080, Würzburg, Germany.
Background: Comprehensive clinical data regarding factors influencing the individual disease course of patients with movement disorders treated with deep brain stimulation might help to better understand disease progression and to develop individualized treatment approaches.
Methods: The clinical core data set was developed by a multidisciplinary working group within the German transregional collaborative research network ReTune. The development followed standardized methodology comprising review of available evidence, a consensus process and performance of the first phase of the study.
Biol Direct
January 2025
National Key Laboratory for Innovation and Transformation of Luobing Theory; The Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Sciences, Jinan, China.
Background: Carotid atherosclerotic plaque is the primary cause of cardiovascular and cerebrovascular diseases. It is closely related to oxidative stress and immune inflammation. This bioinformatic study was conducted to identify key oxidative stress-related genes and key immune cell infiltration involved in the formation, progression, and stabilization of plaques and investigate the relationship between them.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Rheumatology and Immunology, Affiliated Hospital of Yangzhou University, Yangzhou University, No. 368 Hanjiang Middle Road, Yangzhou, Jiangsu, 225000, China.
Rheumatoid arthritis (RA), a chronic inflammatory joint disease causing permanent disability, involves exosomes, nanosized mammalian extracellular particles. Circular RNA (circRNA) serves as a biomarker in RA blood samples. This research screened differentially expressed circRNAs in RA patient plasma exosomes for novel diagnostic biomarkers.
View Article and Find Full Text PDFJ Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
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