The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic
Open accessAbstract
The provision and usage of online and e-learning system is becoming the main challenge for many universities during COVID-19 pandemic. E-learning system such as Blackboard has several fantastic features that would be valuable for use during this COVID-19 pandemic. However, the successful usage of e-learning system relies on understanding the adoption factors as well as the main challenges that face the current e-learning systems. There is lack of agreement about the critical challenges and factors that shape the successful usage of e-learning system during COVID-19 pandemic; hence, a clear gap has been identified in the knowledge on the critical challenges and factors of e-learning usage during this pandemic. Therefore, this study aims to explore the critical challenges that face the current e-learning systems and investigate the main factors that support the usage of e-learning system during COVID-19 pandemic. This study employed the interview method using thematic analysis through NVivo software. The interview was conducted with 30 students and 31 experts in e-learning systems at six universities from Jordan and Saudi Arabia. The findings of this study offer useful suggestions for policy-makers, designers, developers and researchers, which will enable them to get better acquainted with the key aspects of the e-learning system usage successfully during COVID-19 pandemic.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
Abdullah, F., & Ward, R. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.11.036 · Cited by 986 · cited 1×
“Abdullah and Ward (2016) also investigated factors influencing e-learning acceptance using TAM.”
BackgroundModeling Key Drivers of E-Learning Satisfaction among Student Teachers
Teo, T., & Wong, S. L. (2013) · Journal of Educational Computing Research · doi:10.2190/ec.48.1.d · Cited by 77
Cited by
AI‐driven adaptive learning for sustainable educational transformation
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025) · Sustainable Development · doi:10.1002/sd.3221 · Cited by 581
eLearning Acceptance and Adoption Challenges in Higher Education
Ahmad, S., Mohd Noor, A. S., Alwan, A. A., Gulzar, Y., Khan, W. Z., & Reegu, F. A. (2023) · Sustainability · doi:10.3390/su15076190 · Cited by 95
Effects and acceptance of precision education in an AI-supported smart learning environment
Hu, Y.-H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10664-3 · Cited by 68
The emergency (crisis) e-learning as a challenge for teachers in Poland
Tomczyk, Ł., & Walker, C. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10539-7 · Cited by 62
Qazi, M. A., Sharif, M. A., & Akhlaq, A. (2024) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-01-2022-0002 · Cited by 47
Metadata
- Authors
- Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A.
- Venue
- Education and Information Technologies
- Published
- 2020-05-22
- Volume
- 25
- Issue
- 6
- Pages
- 5261-5280
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W3028076230
- Cited by
- 1,205
- References
- 3
- FWCI
- 345.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.