Understanding cross-country differences in assessment simulations: insights from South African and Canadian students
Bibliographic record
Abstract
Abstract AI-based simulations for educational and assessment purposes are gaining global recognition. Informed by cultural comparison research, this study investigates cross-country variations in users’ utilization and perceptions of a simulation-based assessment. Specifically, we conducted a comparative analysis between a sample of South African and Canadian students to uncover potential differences in assessment scores, communication patterns, and reactions vis-a-vis a simulation assessment for evaluating teamwork skills. Data were collected from over 500 undergraduate students in South Africa and Canada who completed a simulation assessment and reported their reactions and perceptions. The findings yielded several noteworthy observations. First, South African students attained higher assessment scores than Canadian students; although, the difference did not quite reach statistical significance at p < 0.05. Second, significant variations were observed in the quantity and style of communication. South African students used fewer words and more polite language, while Canadian students tended to use more decisive language and provided more explanations and help to their virtual teammates. Third, South African students were more likely to perceive their virtual teammates as “human” and were less concerned whether they were real people or virtual. Lastly, compared with their Canadian counterparts, South African students reported more positive reactions and perceived the assessment to be more accurate. These findings warrant further investigation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".