Indigenizing Prior Learning Assessment and Recognition: A Review of the Literature
Bibliographic record
Abstract
The purpose of this paper is to provide a review of existing scholarship on the Indigenization of Prior Learning Assessment and Recognition (PLAR). Providing a careful review of this literature contributes a missing map of this field of scholarship and shares key insights for scholars. This is a timely contribution. While the assessment of prior learning has been in practice for decades, this practice has excluded (and continues to exclude) the knowledges of those who remain underrepresented within higher education. In the case of Indigenous learnings, such exclusions are part of the settler colonial operation of post-secondary education that Indigenous scholars and allies are working to disrupt. In order for post-secondary institutions to remain current and adaptive to the ever-changing process of articulating and accrediting knowledge, recognizing and implementing the Indigenization of PLAR is integral. Readers will gain an improved understanding of PLAR, design and implementation considerations when seeking to Indigenize a PLAR process, and examples of well-implemented PLAR for Indigenous learners. As more and more post-secondary institutions consider (to greater and lesser extents) how to address ongoing colonial exclusion of Indigenous knowledges and learners, an Indigenized PLAR process can offer a useful tool. In the settler state of Canada specifically, where the Truth and Reconciliation Commission’s Calls to Action include a focus on providing adequate funding and training so that post-secondary educators can incorporate Indigenous knowledges and teaching methods into the classroom, Indigenized PLAR could potentially support the work of fulfilling this promise. This paper proceeds as follows: 1) a discussion of the search and analysis methodology; 2) a discussion of the importance of Indigenizing PLAR; 3) an overview of key design lessons drawn from the literature; 4) a discussion of important insights in implementation; and 5) a conclusion.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".