An Integrative Approach to Grading in Competency-Based Curriculum
Bibliographic record
Abstract
BACKGROUND: Competency-based education (CBE) and the American Association of Colleges of Nursing's 2021 Essentials framework necessitate a shift in traditional methods of assessment and evaluation in nursing education. PROBLEM: Establishing a process for converting demonstrated competency levels into equivalent alpha-numeric grades is important to maintain the calculation of a student's grade point average. APPROACH: A faculty task force across 3 undergraduate/prelicensure programs developed an adaptable grading scheme and competency-based grading scale applicable to multiple course settings. OUTCOMES: Adapting the Ottawa Surgical Competency Operating Room Evaluation scale into nursing competency descriptors and aligning the levels with alpha-numeric grades provides a helpful grading structure to operationalize CBE within institutional constraints. This also allows academic freedom for course faculty to design and implement formative and summative assignments based on their relevance to mapped course outcomes. CONCLUSION: CBE frameworks support a progressive and developmentally leveled approach to competency assessment. Integration of educational pedagogies promotes consistency in competency evaluation.
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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 | 0.077 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".