Aviation College is a higher education institution that shifted to e-Learning as the education platform during the COVID-19 Pandemic. This shift has posed challenges, especially in developing countries like the Philippines. This study aims to evaluate students' intentions toward using an e-learning platform at a collegiate aviation institution during the pandemic by employing an integrated extended Technology Acceptance Model (TAM) and Seddon's Information System (IS) Success Model. The study involved 503 college students who completed an online questionnaire with 48 items representing 12 constructs. Structural Equation Modeling (SEM) was utilized to analyze the relationships between variables under extended TAM and IS Success Model. The findings revealed that attitude toward use had the strongest influence on behavioral intention, followed by perceived playfulness. Learning outcomes significantly impacted perceived usefulness, along with information quality, perceived ease of use, and system quality. Additionally, learning outcomes had the greatest effect on user satisfaction, followed by perceived usefulness, information quality, and system quality. Perceived usefulness had a more substantial impact on attitude toward use than perceived ease of use. Regarding perceived ease of use, system quality was the most influential factor, followed by computer self-efficacy and course design. The proposed framework enhances understanding of the relationship between technology adoption theory and the IS success model. The study's findings can help policymakers, software developers, and educators improve the e-learning process and maintain the quality of education.

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308180PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11687720PMC

Publication Analysis

Top Keywords

success model
12
perceived ease
12
system quality
12
e-learning platform
8
aviation institution
8
covid-19 pandemic
8
learning outcomes
8
perceived quality
8
quality perceived
8
ease system
8

Similar Publications

Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers.

View Article and Find Full Text PDF

Statement Of Problem: Although immediate implant loading has shown promising clinical results and high survival rates, an increased risk of implant failure and complications has been reported. Achieving consistently predictable outcomes with this approach remains a challenge, but evidence-based guidelines to assist in selecting suitable patients are lacking.

Purpose: The purpose of this retrospective clinical study was to investigate the success rate, survival rate, and complications of immediate implant loading compared with early and delayed loading.

View Article and Find Full Text PDF

Background: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs.

View Article and Find Full Text PDF

Despite the favorable effects of immunotherapies in multiple types of cancers, its complete success in CNS malignancies remains challenging. Recently, a successful clinical trial of cytokine-induced killer (CIK) cell immunotherapy in patients with glioblastoma (GBM) has opened a new avenue for adoptive cellular immunotherapies in CNS malignancies. Prompt from these findings, herein, we investigated whether dendritic cells (DC) in combination with cytokine-induced killer cells (DC-CIK) could also provide an alternative and more effective way to improve the efficacy of GBM treatment.

View Article and Find Full Text PDF

Eye disease detection has achieved significant advancements thanks to artificial intelligence (AI) techniques. However, the construction of high-accuracy predictive models still faces challenges, and one reason is the deficiency of the optimizer. This paper presents an efficient optimizer named Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction (L-SHACSO).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!