Undergraduate nursing students’ perceptions of computer based testing
Bibliographic record
Abstract
Background & Purpose:The National Council Licensure Examination for Registered Nurses\n(NCLEX-RN) is a computer adaptive licensure examination that nursing students are eligible\nto write upon completion of their undergraduate nursing degree. Success on this exam is a\nrequirement for Registered Nurse practice. Historically, the Canadian licensure examination\nwas a paper-based exam. However, in 2015 the NCLEX-RN was adopted. Initially Canadian\npass rates declined and nursing schools have been seeking strategies to better prepare\nstudents for this exam. Practice with computer based testing (CBT) may be one\napproach. However, CBT has not been widely used and many nursing programs continue\nwith paper-based exams. Recently CBT was integrated into a third year undergraduate\nnursing course in Alberta, Canada. The purpose of this qualitative study was to gain an\nunderstanding of undergraduate nursing student experiences with and perceptions of CBT.\nSpecifically, the goals were to inform personal teaching and evaluative practices, positively\ncontribute to changes to future course testing approaches, and share knowledge with other\neducators who may be considering the integration of CBT into their courses. Sharing this\nknowledge may contribute to the body of literature and serve as a foundation on which\nfurther research on this topic area could be conducted. \nMethod: Through purposive sampling, data was collected from 38 students who completed\na set of reflective questions. Data was analyzed by thematic analysis.\nResults: Four themes were generated: immediacy matters, distrust of self, navigating the\nnew, and high stakes on the horizon. Findings including the benefits, challenges, and\nrecommendations are discussed.\nConclusion: There are advantages and disadvantages to CBT. Providing CBT opportunities\nmay encourage students to reflect on their learning, test-preparation, and test-taking\nstrategies promoting reflection upon how they prepare for and engage in future\ncomputerized exams.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".