Undergraduate nursing students’ perceptions of computer based testing
Bibliographic record
Abstract
Background & Purpose: The National Council Licensure Examination for Registered Nurses (NCLEX-RN) is a computer adaptive licensure examination that nursing students are eligible to write upon completion of their undergraduate nursing degree. Success on this exam is a requirement for Registered Nurse practice. Historically, the Canadian licensure examination was a paper-based exam. However, in 2015 the NCLEX-RN was adopted. Initially Canadian pass rates declined and nursing schools have been seeking strategies to better prepare students for this exam. Practice with computer based testing (CBT) may be one approach. However, CBT has not been widely used and many nursing programs continue with paper-based exams. Recently CBT was integrated into a third year undergraduate nursing course in Alberta, Canada. The purpose of this qualitative study was to gain an understanding of undergraduate nursing student experiences with and perceptions of CBT. Specifically, the goals were to inform personal teaching and evaluative practices, positively contribute to changes to future course testing approaches, and share knowledge with other educators who may be considering the integration of CBT into their courses. Sharing this knowledge may contribute to the body of literature and serve as a foundation on which further research on this topic area could be conducted. \n \nMethod: Through purposive sampling, data was collected from 38 students who completed a set of reflective questions. Data was analyzed by thematic analysis. \n \nResults: Four themes were generated: immediacy matters, distrust of self, navigating the new, and high stakes on the horizon. Findings including the benefits, challenges, and recommendations are discussed. \n \nConclusion: There are advantages and disadvantages to CBT. Providing CBT opportunities may encourage students to reflect on their learning, test-preparation, and test-taking strategies promoting reflection upon how they prepare for and engage in future computerized 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 | 0.004 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".