The Effect of Implementation Nursing Education and Administration Theories Application Using the E-Mind Map on Self-Regulated Learning and Student Academic Performance
Bibliographic record
Abstract
Background: The world uses a wide scope of different advanced applications and services, to be involved across different settings in various circumstances. Educational technology has become the prevailing medium of the era, enabling effective interaction between teachers and learners to facilitate the learning process. The aim of the study was to explore effect of implementation of nursing education and administration theories application using the e-mind map on self-regulated learning and student academic performance. Research Design: `Quasi-experimental research design. Study Setting: The study conducted at the Faculty of Nursing at Helwan University. Subjects : total number of 85 post graduate PHD and doctorate students. Tools of data collection; Data for this research were obtained using three distinct forms First tool: Self ‑regulated learning questionnaire, Second Tools: Student Academic Performance questionnaire. Results: More than nearly all of participants (91.8%) of the studied nursing post-graduate students gained a high level of self‑regulated learning during the post-test phase, followed by the phase of follow-up test most all (83.5%) as compared with the phase of the pre-test less than one quarter. More than nearly all (91.8%) of the nursing post-graduate students studied gained a high level of student academic performance during the post-test phase. The follow-up test phase accounted for more than most of student participants ( 85.9%) , which is substantially near quarter (24.7% )observed during the pre-test phase . Conclusion: Theories using the E-Mind map application had positive large effect size on self-regulated learning and student academic performance. Recommendation: Educational program Supposed be conducted for all nursing students to be more skillful about electronic learning applications, E-mind maps, and self -regulated learning skills .
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".