Effect of Lecture versus Jigsaw Teaching Strategies on Maternity Nursing Students' Attitudes and Academic Achievement
Bibliographic record
Abstract
Introduction: The teaching-learning process is actively engaged in by maternity nursing students through the cooperative learning method known as "jigsaw learning." It also enables them to interact and participate in maternity nursing courses. The study's objective was to compare the att maternity nursing students' attitudes and academic performance in lecture and jigsaw teaching methods. Research Design: A quasi-experimental study design was used. Research Setting: The study was conducted at the Technical Institute of Nursing, Kafer El-Sheikh Governorate, Egypt, during the course entitled "Maternity Nursing." Sampling: A convenience sample of nursing students in the 3rd level and 2nd years was recruited. Number = 160 was categorized into two groups: control group (80) "lecture group" and study group (80) "jigsaw group". Tools: The data was gathered using four tools: 1. self-administered questionnaire, 2. student’s knowledge assessment tool (pre-posttest), 3. Likert attitude scale to assess the student’s attitude toward the teaching strategy, 4. Students' Opinion Sheet. Results: There Both groups had a significant difference in the student’s achievement (post and follow-up written exams). The students' theoretical achievement in maternity nursing lectures was higher in the study groups than in the control group on the post and follow-up exams. Students in the study group exhibited a more positive attitude concerning the teaching strategy than the students who were in the control group. Conclusion: Maternity nursing students' attitudes and performance during the course are improved by using the jigsaw learning strategy. Recommendations: Use the jigsaw learning strategy as a teaching strategy in all academic nursing courses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".