Comparing Results of Alternate Format Test Questions to Standard Multiple Choice Test Questions in First, Second, and Third Year Baccalaureate Nursing Students
Bibliographic record
Abstract
Multiple choice questions are frequently used on examinations within the health disciplines to evaluate student performance.Before 2015, Canadian nursing graduates were required to pass the Canadian Registered Nurse Exam (CRNE) to obtain licensure to practice.This exam consisted of mainly multiple-choice questions.In recent years, Canada (except for its province of Quebec) has adopted the NCLEX-RN exam as the licensure assessment exam for nursing graduates.The NCLEX-RN exam is a computer adaptive test that utilizes not only multiple choice questions, but also question items in a variety of other formats such as multiple responses, fill-in-the-blank calculation, ordered response, and hot spot.To explore if changes in question item formats influenced an individual's ability to answer a test question correctly, we carried out a descriptive, comparative study with first, second, and third-year Baccalaureate nursing students.We compared the students' performance on NCLEX-RN style alternate format test questions with their performance on standard multiple choice questions in an anatomy and physiology course.We further compared their performance on both types of questions and their final grade in the course.A convenience sample of students enrolled in one Atlantic Canadian Nursing Program was included.Descriptive statistics resulted in significant differences between the various types of questions used.The results of this study provide the basis for recommendations directed toward the inclusion of alternate format test questions on examinations throughout the current nursing curriculum as a means to enhance baccalaureate nursing students' performance on their future licensure exams.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".