Placing authenticity at the heart of student self-assessment: an integrative review
Bibliographic record
Abstract
Self-assessment involves students making judgements about their own learning. Self-assessment is promoted widely due to its benefits for lifelong learning. However, students often find self-assessment mechanical, useless and redundant – indeed inauthentic. This may partly result from understanding self-assessment as an instrumental and acontextual practice. We take an alternative approach by focusing on the authenticity of self-assessment. We bring together two research areas that have rarely intersected: self-assessment and authentic assessment. How has research conceptualised authenticity with respect to self-assessment? What could we learn from earlier studies to consider authenticity more meaningfully in self-assessment design? To answer these questions, we conduct an integrative review of 40 studies. We formulate an organising framework that outlines the various dimensions of authenticity in self-assessment. We argue that authenticity is a powerful idea that may bring self-assessment from the margins of higher education to its very centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".