Factors Affecting in Achievement of Universal Courses Objective by Using Distance Education during COVID-19 Pandemic
Bibliographic record
Abstract
The Coronavirus pandemic (COVID-19) has caused countries to resort to using distance education, as the study tried to uncover the factors that affect in the achievement of universal courses objective by using distance education during (COVID-19) pandemic. The researchers used the relational descriptive Methodology through the questionnaire Instrument. The questionnaire consisted of (24) items distributed on (6) factors (Effectiveness of Students, Effectiveness of faculty members, Methods of Distance Teaching, Motivation of Students, Availability of facilities and Achieving of course objectives). The study sample consisted of (1320) students from university colleges in Jordan, which were chosen by the Available sample method. The appropriate statistical analysis represented by the path analysis was found through the IBM SPSS Statistics. The results of the study showed the validity of the study hypotheses and that the specific factors (Effectiveness of Students, Effectiveness of faculty members, Methods of Distance Teaching, Motivation of Students and Availability of facilities) affect the Achieving of course objectives. According to the results of the research, the researchers recommended concerned universities in taking the factors found in the study to improve the effectiveness of distance education. Where the research contributes to supporting e-learning by developing it as a method of distance education and identifying the most important factors that affect its achievement of the objectives of the courses, especially in countries where the use of e-learning is considered a preliminary experience due to the Corona pandemic, which has not happened previously, and the state did not resort Jordanian to rely on him previously.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".