Team-based learning: Application in undergraduate baccalaureate nursing education
Bibliographic record
Abstract
Objective: The aim of the study was to determine the efficacy of Team-based Learning (TBL) in an undergraduate nursing course with regard to the outcomes of academic performance (Health Education Systems Incorporated [HESI ® ] Management exam) and self-reported measures of critical thinking, leadership and management skills, overall course ratings, accountability to learning, preference for lecture or TBL, and learner satisfaction with TBL. Methods: In a quantitative, quasi-experimental post-test study, 221 undergraduate senior nursing students participated in the TBL course or a traditional instructor-led control course. In both courses, academic performance was measured by the HESI ® Management scores; critical thinking, leadership and management skills, and overall course experience were measured using an online survey. In the TBL course, accountability to learning, preference for lecture or TBL, and learner satisfaction was measured with the Team-Based Learning Student Assessment Instrument. Results: When compared to lecture, TBL learners scored significantly higher on the HESI ® Management exam and reported significantly higher critical thinking, leadership and management skills and better overall course experience ratings ( p ≤ .01). TBL learners reported moderate to high levels of accountability, higher preference for TBL than lecture, and satisfaction with TBL. Total scores indicated moderate to high levels of favorable experiences with TBL. Conclusions: Results indicate TBL is an acceptable and efficacious instructional strategy in undergraduate nursing students. To control for extraneous factors and limit confounding, future research should evaluate the impact of TBL via a randomized control trial.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".