Attitudes of the Students Enrolled in the Introduction to Education Course Towards E-learning, Its Applications, and Its Relationship to Some Variables
Bibliographic record
Abstract
This study aimed at identifying the attitudes of students enrolled in the Introduction to Education course at Karak University College towards e-learning and its applications in light of its relationship to some variables. The population of the study consisted of students enrolled in the Introduction to Education course in the Department of Educational and Social Sciences at Karak University College in the first semester of the academic year (2020 -2021) The study was applied to the entire population of the study, whose number was (75) male and female students. The study used the descriptive approach and applied a scale to identify students' attitude towards e-learning and its applications. The tool consisted of (39) items and graded on a five-degree scale. The results of the study showed that the students ’attitudes towards e-learning and its applications came as follows. 12 paragraphs of the scale were within the positive high attitudes, while the remaining 27 paragraphs had a neutral attitude and there were no negative attitudes. The mean scores of the scale was (2.94), which indicated that the students ’attitudes were overall neutral. The results of the study also showed that there were no statistically significant differences between the responses of the study sample about their attitudes towards e-learning and its applications according to their academic achievement. The results also showed no statistically significant differences between students' responses about their attitudes towards e-learning and its applications according to their different experiences in the fields of e-learning, and in favor of the sample members with average experience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".