The Role of Students’ Assessment Literacies in Navigating University Assessment, GenAI, and Academic Integrity
Bibliographic record
Abstract
Academic integrity concerns related to students’ use of technology have renewed calls for teaching, assessment, and learning best practices, including those that involve and empower students. Empowerment is a benefit of developing students’ assessment literacies, or how students contextually understand, plan, and undertake assessment and use assessment information to monitor and progress their learning. Informed by Bandura’s (1986) social cognitive theory and reflexivity (Dewey, 1933; Schön, 1983), a qualitative exploratory case study examined first-year university students’ experiences with assessment and the development of their assessment literacies. The findings highlighted student autonomy and empowerment benefits while stressing the importance of reflexivity and assessment literacies for both students and teachers. Teaching, assessment, and learning best practices commonly suggested to promote academic honesty in the GenAI context were also evident. Accordingly, this paper explores the role of students’ assessment literacies as part of these best practices, with implications for all levels of education.Keywords: student assessment literacies, academic integrity, GenAI, student empowerment, transition to university
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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".