Hybrid learning enables educators to incorporate elements of conventional face-to-face learning methods with structured online schemes. This study aimed to assess university students' perceptions of online and hybrid learning during the ongoing COVID-19 pandemic. A web-based cross-sectional study was conducted at the University of Sharjah, in the United Arab Emirates (n = 2056). Students' sociodemographic characteristics, perceptions of online and hybrid learning, concerns, and university life changes, were investigated. Perception statements were dichotomized into "positive" and "negative" based on a 50% cut-off point. Scores of > 7 and >5 indicated positive perceptions of online and hybrid learning respectively while scores of ≤ 7 and ≤ 5 indicated negative perceptions. Binary logistic regression analysis was performed to predict students' perceptions of online and hybrid learning according to demographic variables. Spearman's rank-order correlation was performed to determine the relationship between students' perceptions and behaviors. Most students preferred online learning (38.2%) and on-campus learning (36.7%) to hybrid learning (25.1%). Around two-thirds of the students had a positive perception of online and hybrid learning in terms of university support, however, half of them preferred the assessment during online or on-campus learning. Main difficulties reported in hybrid learning were lack of motivation (60.6%), discomfort when on-campus (67.2%), and distraction due to mixed methods (52.3%). Older students (p = 0.046), men (p<0.001), and married students (p = 0.001) were more likely to have a positive perception of online learning, while sophomore students were more likely to have a positive perception of hybrid learning (p = 0.001). In this study, most students preferred online or on-campus over hybrid learning and expressed certain difficulties while on hybrid learning. Future research should focus on investigating the knowledge and capability of graduates from a hybrid/online model compared to a traditional model. Obstacles and concerns should be considered for future planning to ensure the resilience of the educational system.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047520 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283513 | PLOS |
J Chem Theory Comput
January 2025
Exscientia, Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level.
View Article and Find Full Text PDFComput Biol Med
January 2025
Division of Electronics and Information Engineering, College of Engineering, Jeonbuk National University, 567, Baekje-daero, Deokjin-gu, 54896, Jeonju, Republic of Korea. Electronic address:
Kidney stone is a common urological disease in dogs and can lead to serious complications such as pyelonephritis and kidney failure. However, manual diagnosis involves a lot of burdens on radiologists and may cause human errors due to fatigue. Automated methods using deep learning models have been explored to overcome this limitation.
View Article and Find Full Text PDFComput Biol Med
January 2025
Department of Mathematics, NED University of Engineering & Technology, Pakistan. Electronic address:
For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. The proposed concept is applied to the heart disease dataset for the assessment of heart disease risk in individuals. By comparison of three binary (0,1) input variables, the variables' uncertainties and their collective impact can be analyzed that provide complete information leading to better outcome prediction.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Faculty of Mathematics and Natural Sciences , Hochschule Darmstadt, Schöfferstr., 3, Darmstadt, Hessen, 64295, GERMANY.
Magnetic Particle Imaging (MPI) is an emerging medical imaging modality which has gained increasing interest in recent years. Among the benefits of MPI are its high temporal resolution, and that the technique does not expose the specimen to any kind of ionizing radiation. It is based on the non-linear response of magnetic nanoparticles to an applied magnetic field.
View Article and Find Full Text PDFComput Biol Chem
January 2025
School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China. Electronic address:
Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and implicated in the numerous diseases. By exploring the subcellular localizations of lncRNAs, we can not only gain crucial insights into the molecular mechanisms of lncRNA-related biological processes but also make valuable contributions towards the diagnosis, prevention, and treatment of various human diseases. However, conventional experimental techniques tend to be laborious and time-intensive.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!