Selecting and grouping students effectively – increasing intra-group diversity and inter-group equality
Bibliographic record
Abstract
Group work is a collaborative experience that teach valuable interpersonal skills. It offers great opportunity to address and consider students’ backgrounds, interests and performance in different ways. For poster presentations, for instance, communicative competence is critical, and in Western cultures, expectations go beyond English language proficiency. It also includes eye contact, assertive voice timbre and posture and gestures that reflect confidence and knowledge. Group work allows students from different backgrounds to share these skills and learn from each other. Allocation of members into groups is often done by self-selection or randomly. Self-selected groups are biased towards friendship but tend to be easier to administer. Random allocation is perceived as fair by the students, but at the same time, it is an overused approach. Random group allocation resolves the friendship bias and helps in balancing student heterogeneity. However, the randomness of the process does not guarantee academically balanced inter-groups. Alternatively, students can be grouped based on their academic performance. However, not by clustering high- or low-performing students (ability grouping), but rather equally distributing them across different groups, with each group including students with a broad grade distribution. We used this latter group allocation strategy for a Virology poster assignment for third-year students at UWA. It was hypothesised that it would be a fairer student distribution, with possible improvement in academic performance across the class. We observed an increase in the class overall average score, with a 3-fold increment in top-HD marks (grades >90%), compared to last year, in which groups were assigned randomly. Students reported to have learned communication and leadership skills from peers; and believed their final mark for the assignment was fair based on their contribution to the group. We observed that deliberately mixing students with different academic performance encouraged cooperative learning and benefited both high- and low-performing students.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".