COVID-19 fears and e-learning platforms acceptance among Jordanian university students
Bibliographic record
Abstract
Broadening the approval and usage of technology to study online education is not a novel study subject, and several researchers have addressed it. However, the production of a systematic Technology acceptance model capable of examining online education adoption in the current Covid-19 is seen as a vital research path. Literature research was conducted to evaluate the most used external influences of innovation adoption regarding online learning acceptance. The search revealed that computer self-efficacy, corona apprehension, perceived ease of use, and perceived usefulness are the external factors for technology acceptance. The purpose of this paper is to investigate the variables that online education programs' approval among students can influence. 185 students from Jordan's Al-Zaytoonah University and Applied Science Private University participated in the online research. The online questionnaire system in this report was analyzed using SmartPLS tools. According to the findings, perceived usefulness, behavioral intent of use, self-efficacy, and Corona fear all positively affect the adoption of online education programs. The findings of this study were used as a required input in the latest online education interactive analytical production that was used extensively during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".