Fairness In Classroom Assessments at Individual and National Culture Levels: A Cross-Cultural Empirical Examination
Bibliographic record
Abstract
This dissertation examines, for the first time, the effect of cultural values on students' perceptions of (un)fairness in classroom assessments (CA) across two phases, considering both individual and national culture levels. Hofstede’s cultural framework and organizational justice theory serve as the central theoretical foundation for this dissertation. Empirically, a two-phase quantitative methodological study was conducted. Phase I utilized a comprehensive survey distributed among all five U15 institutions—University of Saskatchewan, University of Manitoba, University of Alberta, University of Calgary, and University of British Columbia—via the SurveyMonkey platform (N=626). After accounting for the direct impact of demographic characteristics, the results of hierarchical multiple regression analysis revealed that both the power-distance index and the uncertainty-avoidance index significantly predicted students’ perceptions of CA (un)fairness. In Phase II, the findings from Phase I were extended to the national culture level using two achieved datasets: the 2015 PISA (i.e., Programme for International Student Assessment) and the 2015 Hofstede datasets. After matching the data from both archived datasets, 60,004 students from 2,957 schools across 20 countries participated in this phase. The results of the HLM analysis indicated that none of the cultural values predicted students’ perceptions of CA (un)fairness at the national culture level. Overall, the findings from both phases of this dissertation demonstrate that various variables, including cultural values (i.e., power distance and uncertainty avoidance) at the individual level, significantly predict shaping the student’s perception of CA (un)fairness. These individual-level variables include demographic characteristics (i.e., gender, SES, immigrant status), psychosocial predictors (i.e., test anxiety, achievement motivation, experience with bullying), classroom predictors (i.e., perceived feedback, adaptive instruction, teacher support), and cultural values (i.e., power distance and uncertainty avoidance). The findings reveal significant variations in perceived fairness in CA contexts linked to these factors, providing insights into how assessment practices can be enhanced to align with diverse student needs. This study contributes to a broader understanding of CA fairness in educational contexts, offering actionable recommendations for educators and policymakers to promote equitable and culturally responsive assessment environments.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".