The Development of Students' Assessment Literacies as They Transition to University: An Exploratory Case Study
Bibliographic record
Abstract
Changes in academic demands, expectations, and ways of demonstrating knowledge through assessments are among the challenges faced by students transitioning to university.There is transition research in the Canadian context, but little documenting students' experiences with assessment, or how they develop their assessment literacies (e.g., understanding assessment in the course context and how assessment information is used to monitor and improve learning).This research examined how first-year university students' experiences with, knowledge of, and expectations about assessment impacted the development of their assessment literacies as they transitioned to university.The exploratory case study was theoretically framed by social cognitive theory (Bandura, 1977a, 1986) and reflexivity (e.g., Ryan, 2015;Schön, 1983Schön, , 1987)).Three data sources were collected from ten first-year student participants: course assessment documents that triangulated students' responses from two semi-structured interviews and students' assessment journal entries.These data were coded using Saldaña's (2016) structural, emotion, and values coding.Course assessment documents were compared and categorized (Maxwell & Miller, 2008), noting the social setting and social actors (stakeholders) involved (Coffey, 2014).
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".