The relationship between learner-centered teaching and learning motivation among nursing students in Minia University
Bibliographic record
Abstract
Background and objective: One of the important goals of nursing education is to motivate nurses to acquire skills for providing proper quality of health care services to clients with various complex health problems. Accomplishing this has challenged educational organizations for long years. The aim of the present study is to identify the relationship between Learner-Centered Teaching and learning motivation among Nursing Students in Minia University.Methods: A quantitative-correlational research design was utilized in the present study. The study sample comprised all fourth-year nursing students who were available at the time of data collection (N = 168). For the assessment of Learner-Centered Teaching Practices and learning motivation, a questionnaire developed by Rossi (2009) was used for data collection.Results: The study participants reported their highest mean scores about learner-centered teaching practices with three domains as follows: facilitates the learning process, provides for individual and social learning needs, and establishes positive interpersonal relationships. Also, they expressed highest mean scores about learning motivation with intrinsic motivation factors. Additionally, the study findings presented high statistical significant difference and fair positive relationship between all subscales of learner-centered teaching practices and intrinsic motivation factors (p value = .001).Conclusions: Nursing curriculum should include learner as active contributors in the learning process. When nurse educators give a lecture and encourage students to interact actively with ideas, and information after the lecture, this will motivate students to be an active learner, as well as, inspire innovative thinking and creativity of the students.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".