Correlation of formative assessments as the means of predicting summative performance in paramedic students
Bibliographic record
Abstract
Background: Paramedic programs use formative assessments to determine cognitive competency. Understanding the number of failed formative units as a probability of passing the summative exam will allow programs to set additional benchmarks. The purpose of this study was to determine whether failure in formative exams determines success on a summative exam. Methods: Formative and summative scores from 2011 – 2016 for paramedic students with accounts in Fisdap™, an Internet-based administrative database, were retrospectively reviewed for the following criteria: provided consent for research, completed all six formative (unit) examinations, and completed a summative (comprehensive) examination. Analyses were performed with Pearson correlations and linear regression. Results: A total of 1,406 student records were included based on inclusion criteria. Correlation with each formative and the summative examination were all significant, p < 0.001: Cardiology 0.597; Airway 0.571; Medical 0.571; Trauma 0.566; Ob/Pediatrics 0.549; Operations 0.495. The cardiology exam was shown to have a moderate correlation on summative performance, whereas the operations exam had the weakest correlation. The number of formative examination failures was a significant predictor of the probability of passing the summative examination, t(1405) = –31.02, p < 0.001. Zero failed unit examinations yielded a 100% probability of passing. Three failed formative exams yielded a 60.4% probability. Four failed attempts yielded a 44.8% probability. Failure of all six formative exams yielded a 13.4% probability of passing the Paramedic Readiness Exam Version 3. Conclusion: Not all formative examinations hold the same predictive power on the probability of passing a summative examination. Each had their own correlation value. Students who did not fail formative examinations have a 100% likelihood of passing the summative examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".