Self-assessment design in a digital world: centring student agency
Bibliographic record
Abstract
Digital technologies allow student self-assessment to be adaptive, scalable and multimodal. Despite such technological advances, digital self-assessment practices have largely reinforced the existing norms around student roles, leaving the fundamentals of self-assessment design untouched. Digital self-assessment has centred on learning outcomes and self-regulation with less attention to students’ own initiatives, aspirations and roles – their agency. Ironically, the ‘self’ has remained at the margins of digital self-assessment work. In this conceptual study, we propose ‘the digital’ as a catalyst to rethink the student role in self-assessment. This way, the digital could address fundamental issues with self-assessment already present in the pre-digital age. We explore three ways in which digital self-assessment could promote student agency. By reviewing critical examples of literature on self-assessment and digital technologies, we propose that the digital in self-assessment may be seen: (1) as a tool for promoting students’ self-regulation, as understood in individualistic terms; (2) as a means to develop students’ digital agency; and (3) as a means to provide students with agency over their identity formation in the digital world. These three ideas may guide future work to ensure self-assessment design is relevant for students’ increasingly digital futures, particularly in an era of Artificial Intelligence.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".