Bibliographic record
Abstract
In engineering programs of study, students often work in small to medium size groups. In particular, programs designed on the project –based learning principle rely heavier on structured group work for projects and integrated courses. There are incontestable benefits surfacing from group work, particularly seen as the increase of critical thinking and problem solving skills and development of social interaction abilities.Challenges occur more often than expected with the assessment and evaluation of the individual performance and participation of each group member in contrast with the whole group results. The final result can be outstanding but it might realistically belong to only one or two group members. The result of the individual assessment must not only reflect accurately and fairly one’s effort but also fit properly in the whole group diversity landscape. Alternatively, group members may have valuable but rather inconspicuous contributions that might easily be undetected and go unrecognized.How to identify sooner rather than later the non-participating students within the group and correct the situation? Which is the best method of fair detection and praise for considerable contribution? The extraction of peer evaluation data and its incorporation in the overall group assessment, represents another often difficult or misinterpreted task. These are questions and challenges yet to be properly addressed. This paper provides a synopsis of existing evaluation techniques for engineering students working in groups, both from a psychological and academic point of view, including examples of current practices from existing project –based learning programs.
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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 | 0.034 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".