Application of National Education Technology Standards as Perceived by Nursing Students and Its Relation to Their Problem Solving Skill during COVID 19 Disaster
Bibliographic record
Abstract
With the emergence of COVID 19 disaster, dependence on technological and electronic learning is increasing. National Education technology standard has a great impact on improving students' skills. One of these skills is problem solving which is very crucial to nurse student to be prepared to be professional nurse. This study sought to assess application of national education technology standards as perceived by nursing students and its relation to their problem solving skill during COVID 19 disaster. The study adopted a descriptive correlational design using a convenience sample (N = 218) of all fourth nursing students who accept to participate in the study at Faculty of Nursing, Menoufia University. The instruments used to gather the data were developed questionnaire by researchers to assess application of national education technology standards, and problem solving skill questionnaire. The results show that the majority of nursing students have high level of perception regarding application of these standards. Moreover, the high percentage of nursing students had high level problem solving skill, and there was a positive moderate correlation between total score national education technology standards, and total score problem solving skill. Based on the findings, it is very important to ensure application of national education technology standards for teaching staff and administrative system. Moreover, Periodic updates and training on the new changes in education technology for both nursing students and teaching staff.
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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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".