The academic portfolio: Validation of the learning journey
Bibliographic record
Abstract
As academicians, students’ learning achievements must be described to ascertain programmatic quality and professional accreditation. Additionally, demonstrating accountability is pivotal for application to nursing students’ learning in patient situations and leadership decision-making. In this process review, nursing student learning was validated by the use of academic portfolios. Learning was authenticated by having the students identify evidence of their learning applied to school of nursing and program outcomes for nursing administration, the Essentials of Master’s Education in Nursing, and the American Organization for Nursing Leadership (AONL) competencies. The AONL competencies were a crucial measurement which enabled students to better view the full scope of their roles as nursing leaders. These academic portfolios provided illustrative vignettes of application of students’ learning throughout the master in nursing administration program. Academic portfolios personify the students’ learning experiences to validate student learning for academic reviewers, provide continuous quality improvement for nursing programs, and guide the learning application to the students’ future careers and lives. These results will also demonstrate program success during an upcoming accreditation survey thus fulfilling a triple objective with one academic project: assessment of student goal achievement, program goal attainment, and accreditation preparedness.
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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 | 0.116 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".