Creating and enacting culturally responsive assessment for First Nations students in higher education settings
Bibliographic record
Abstract
In this article, we argue that current assessment practices in higher education require urgent examination and should be re-imagined in culturally responsive ways to ensure fairness for all. From sociocultural and social justice perspectives, we highlight examples of cultural and linguistic bias in assessment that disadvantages many First Nations students. Incorporating a constructivist viewpoint, we argue that assessment practices must keep pace with culturally responsive pedagogical practices to improve assessment validity for First Nations students and to maintain constructive alignment between learning, teaching, and assessment. Based on qualitative interviews with stakeholders in the On Country Teacher Education program, we describe how university lecturers changed their approaches to assessment and modified their assessment tasks to enact and create culturally responsive assessments. These practices, whilst beneficial for First Nations students, are viewed as being ‘responsive’ rather than ‘proactive’. Recommendations include shifting to a ‘proactive’ stance by evaluating the validity of student learning outcomes and assessment design from the onset.
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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 | 0.139 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".