The Group-based Assessment Approach in Nursing Education: The Perspective of Nursing Students on Group-based Assessment Process at a Namibian University
Bibliographic record
Abstract
Group-based assessments used in the Bachelor of Nursing Science (clinical) Honours programme at a public university in Namibia are usually in the form of assignments and projects. Completing tasks in groups helps students to develop important skills like critical thinking and debating. In addition, it prepares them to work in the health-care environment where collaboration with others is required. That said, nursing students lack cooperation during the process of completing their group assignments or projects. A classroom-based research was conducted using action research as the design. The objectives were to: explore what is causing lack of cooperation during group-based assessment as perceived by nursing students, and to propose, implement and evaluate measures to improve cooperation during group-based assessment task completion. Themes that emerged as factors contributing to a lack of cooperation are: student motivation, student personal characteristics, lack of planning to approach the allocated task, student learning approaches, communication-related issues, and group composition and allocation procedures. The proposed measures of students to ensure cooperation are: selection of group leaders, determining lecturer roles in facilitating group assessment, improving communication, and involvement of students in the allocation procedures of group members.All suggestions were successfully implemented. Evaluation of measures to ensure cooperation revealed that students appreciated the group-based approach strategy given its very positive impact on their learning.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".